DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status Claims 1-20 are currently pending and under examination herein. Claims 1-20 are rejected. Priority The instant application claims priority as a continuation of PCT/ CN2022 /073158 filed 01/21/2022 and foreign priority to CN202110112207.8 filed 01/24/2021 . A certified copy of CN202110112207.8 was not found within the file documents. In this action, claims 1-20 are examined as though they had an effective filing date of 01/24/2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s). Information Disclosure Statement The information disclosure statements (IDS) submitted on 10/05/2022 and 04/18/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Drawings The drawings filed on 10/05/2022 are accepted. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claims 3-4 and 15-16 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim s 3 and 15 and recites the limitation " the key breaking information ". There is insufficient antecedent basis for this limitation in the claim, as there is no mention of key breaking information in claims 1-2 or 13-14, which claims 3 and 15 depend on. Claims 4 and 16 depend on Claims 3 and 5 and thus contain the above issues due to said dependence . Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1 : YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law ( Step 2A , Prong 1 ). Claims 1-12 are directed to a method and claims 13-20 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea: Claim 1 recites the limitation - expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes; the target retrosynthesis model comprising a reactant generation network, the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position. Based on the broadest reasonable interpretation, obtaining and predicting could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 1 also recites recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Based on the broadest reasonable interpretation , determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 2 recites the limitation - determining a first string of the compound molecule corresponding to the first leaf node; filtering the at least one synthon based on a preset rule to determine the predicted molecule set . Based on the broadest reasonable interpretation , determining and filtering could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 2 also recites converting the first string into a first molecular map; determining the bond breaking position of the compound molecule corresponding to the first leaf node according to the first molecular map ; determining at least one synthon according to the bond breaking position through the reactant generation network. Based on the broadest reasonable interpretation, determining and converting could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 3 recites the limitation - determining bond breaking information according to the first molecular map and a limit on a number of broken bonds . Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 3 also recites determining a target key feature according to the target molecule, the target key feature comprising an atom key feature and a bond key feature, the atom key feature comprising at least one of an atom type, a number of bonds, a formal charge, chirality, a number of hydrogen atoms, an atomic hybridization state, aromaticity, an atomic weight, a high frequency reaction center feature, and a reaction type, and the bond key feature comprising at least one of a bond type, a conjugate, a ring bond, and a molecular stereo chemical feature; extracting the key breaking information based on the target key feature to determine the bond breaking position. Based on the broadest reasonable interpretation, determining and extracting could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 4 recites the limitation - determining a node feature and an edge feature of the first training molecule and the first training synthon, the node feature being used for indicating a relationship between atoms of the first training molecule and the first training synthon, and the edge feature being used for indicating a relationship of a chemical bond between the first training molecule and the first training synthon . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 4 also recites determining a first loss function based on the node feature and the edge feature ; determining a bond breaking probability of the chemical bond between the first training molecule and the first training synthon; determining a second loss function based on the bond breaking probability . Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 5 recites the limitation - splitting the target molecule based on the bond breaking position to obtain at least one synthon molecular map; converting the synthon molecular map into a second string ; determining the at least one synthon according to the third string through the reactant generation network . Based on the broadest reasonable interpretation, obtaining, converting, and determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 5 also recites obtain a third string . Based on the broadest reasonable interpretation, obtaining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 6 reties the limitation - determining a first training string based on a string corresponding to the second training molecule, a string corresponding to the second training synthon, and the preset reaction type ; determining a second training string corresponding to the training reactant; associating the first training string and the second training string to determine a first training sample pair . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 6 also recites training the reactant generation network based on the first training sample pair . Based on the broadest reasonable interpretation, training could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 7 recites the limitation - adding the candidate string into the string corresponding to the second training synthon to update the first training string to a third training string; training the reactant generation network based on the second training sample pair . Based on the broadest reasonable interpretation, updating and training could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 7 also recites associating the third training string and the second training string to determine a second training sample pair . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 8 recites the limitation - determining a target character format; and updating the first training string based on the target character format. Based on the broadest reasonable interpretation, determining and updating could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 9 recites the limitation - determining a first candidate molecule to which the predicted molecule set corresponding to the second leaf node corresponds under a preset reaction type ; expanding the first candidate molecule through the target retrosynthesis model to obtain third leaf nodes ; recursively processing the predicted molecule set corresponding to the third leaf nodes to determine a second candidate molecule . Based on the broadest reasonable interpretation, determining and obtaining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 9 also recites determining that a leaf node corresponding to the second candidate molecule is the terminal node in response to the second candidate molecule satisfying the preset condition . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 10 recites the limitation - traversing based on the second candidate molecule to obtain a plurality of pathway molecules ; determining that the second candidate molecule satisfies the preset condition and determining that the leaf node corresponding to the second candidate molecule is the terminal node, in response to the pathway molecule being a molecule in a basic molecule set. Based on the broadest reasonable , obtaining and determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 11 recites the limitation - determining a number of expansions corresponding to the second candidate molecule in the tree structure ; determining that the second candidate molecule satisfies the preset condition and determining that the leaf node corresponding to the second candidate molecule is the terminal node, in response to the number of expansions reaching a preset value . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 12 recites the limitation - a compound molecule corresponding to the second leaf node is obtained by screening based on at least one of functional group interference, use of a protective group, a reaction feature of a functional group, and a name reaction rule. Based on the broadest reasonable interpretation, obtaining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 13 recites the limitation - expand the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes ; the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position . Based on the broadest reasonable interpretation, determining and obtaining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 13 also recites recursively process the predicted molecule set corresponding to the second leaf nodes and determine a terminal node that satisfies a preset condition; traverse path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 14 recites the limitation - determine a first string of the compound molecule corresponding to the first leaf node ; filter the at least one synthon based on a preset rule to determine the predicted molecule set . Based on the broadest reasonable interpretation, determining and filtering could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 14 also recites convert the first string into a first molecular map; determine the bond breaking position of the compound molecule corresponding to the first leaf node according to the first molecular map ; determine at least one synthon according to the bond breaking position through the reactant generation network . Based on the broadest reasonable interpretation, determining and converting could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 15 recites the limitation - determine bond breaking information according to the first molecular map and a limit on a number of broken bonds . Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 15 also recites determine a target key feature according to the target molecule, the target key feature comprising an atom key feature and a bond key feature, the atom key feature comprising at least one of an atom type, a number of bonds, a formal charge, chirality, a number of hydrogen atoms, an atomic hybridization state, aromaticity, an atomic weight, a high frequency reaction center feature, and a reaction type, and the bond key feature comprising at least one of a bond type, a conjugate, a ring bond, and a molecular stereo chemical feature; extract the key breaking information based on the target key feature to determine the bond breaking position . Based on the broadest reasonable interpretation, determining and extracting could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 16 recites the limitation - determine a node feature and an edge feature of the first training molecule and the first training synthon, the node feature being used for indicating a relationship between atoms of the first training molecule and the first training synthon, and the edge feature being used for indicating a relationship of a chemical bond between the first training molecule and the first training synthon . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 16 also recites determine a first loss function based on the node feature and the edge feature; determine a bond breaking probability of the chemical bond between the first training molecule and the first training synthon; determine a second loss function based on the bond breaking probability . Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 17 recites the limitation - split the target molecule based on the bond breaking position to obtain at least one synthon molecular map; convert the synthon molecular map into a second string; determine the at least one synthon according to the third string through the reactant generation network. Based on the broadest reasonable interpretation, determining , obtaining, and converting could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 17 also recites update the second string based on a preset reaction type to obtain a third string . Based on the broadest reasonable obtaining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 18 recites the limitation - determine a first training string based on a string corresponding to the second training molecule, a string corresponding to the second training synthon, and the preset reaction type; determine a second training string corresponding to the training reactant; associate the first training string and the second training string to determine a first training sample pair . Based on the broadest reasonable interpretation, determining could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 18 also recites train the reactant generation network based on the first training sample pair . Based on the broadest reasonable interpretation, training could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 19 recites the limitation - add the candidate string into the string corresponding to the second training synthon to update the first training string to a third training string; train the reactant generation network based on the second training sample pair. Based on the broadest reasonable interpretation, updating and training could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 19 also recites associate the third training string and the second training string to determine a second training sample pair . Based on the broadest reasonable interpretation, associating could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 20 recites the limitation - expand the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes ; a reactant generation network ; and the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position . Based on the broadest reasonable interpretation, determining could include equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. Claim 20 also recites recursively process the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and traverse path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Based on the broadest reasonable interpretation, determining and traversing could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. These limitations recite concepts of determining, predicting, modeling, simulating, and identifying information and applying or training machine learning algorithms that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. These recitations are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-20 recite an abstract idea and law of nature ( Step 2A , Prong 1: YES ). Claims found to recite a judicial exception under Step 2A , Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not ( Step 2A , Prong 2 ). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements: Claim 1 recites the method performed by a computer device; obtaining a target molecule and determining the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule; the target retrosynthesis model comprising a graph neural network; the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node, Claim 2 recites through the graph neural network Claim 3 recites through the graph neural network Claim 4 recites obtaining a first training molecule and a first training synthon corresponding to the first training molecule; updating a model parameter of the graph neural network according to the first loss function and the second loss function. Claim 5 recites updating the second string based on a preset reaction type . Claim 6 recites obtaining a second training molecule, a second training synthon, and a training reactant . Claim 7 recites obtaining a candidate string predicted by the graph neural network . Claim 12 recites wherein the target molecule is a drug molecule, the tree structure is a Monte Carlo tree . Claim 13 recites a n apparatus for predicting retrosynthesis of a compound molecule, comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code ; obtaining code, configured to cause the at least one processor to obtain a target molecule and determine the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule ; expansion code, configured to cause the at least one processor to ; processing code, configured to cause the at least one processor to ; prediction code, configured to cause the at least one processor to ; the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node . Claim 14 recites through the graph neural network . Claim 15 recites bond breaking code configured to cause the at least one processor to ; through the graph neural network ; key breaking extraction code configured to cause the at least one processor to . Claim 16 recites wherein the graph neural network is trained by training code configured to cause the at least one processor to: obtain a first training molecule and a first training synthon corresponding to the first training molecule; update a model parameter of the graph neural network according to the first loss function and the second loss function. Claim 17 recites wherein the expansion code is configured to ; update the second string based on a preset reaction type . Claim 18 recites by training code configured to: obtain a second training molecule, a second training synthon, and a training reactant . Claim 19 recites obtain a candidate string predicted by the graph neural network . Claim 20 recites a non-transitory computer-readable storage medium, storing a computer program that when executed by at least one processor causes the at least one processor to: obtain a target molecule and determining the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule ; the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node . There are no limitations that indicate that the claimed determining, predicting, modeling, simulating, and identifying information and applying or training machine learning algorithms require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. It should be noted that while implementations of the neural network w ere considered additional elements, the reaction generation network was not explicitly defined as a neural network, and no specific architecture was described. Therefore, the reaction generation network was considered a general machine learning model and its implementation was considered a judicial exception. As such, claims 1-20 are directed to an abstract idea ( Step 2A , Prong 2 : NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities. As discussed above, there are no additional limitations to indicate that the claimed determining, predicting, modeling, simulating, and identifying information and applying or training machine learning algorithms require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Feng et al. (2018, Frontiers in Chemistry, Vol. 6: 1-10) teaches that the use of neural networks, including graph based neural networks , have become standard in the field of synthesis and retrosynthesis of complex molecules (Page 7, Column 1, Paragraph 2: In recent years, deep neural networks have been applied to this field. One characteristic feature is that computers do not need to follow human-defined reaction rules, and instead, they can recomprehend chemical reactions with only millions of reaction examples ) . The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself ( Step 2B : No ). As such, claims 1-20 are not patent eligible. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1-4, 9-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Segler et al. (2018, Nature, Vol. 555: 604-619, IDS filed 04/18/2024), in view of Jin et al. (2017, Advances in Neural Information Processing Systems 30: 1-10). Italicized text from reference art. The applicable claims include: Claim 1. A method for predicting retrosynthesis of a compound molecule, performed by a computer device, the method comprising: i . obtaining a target molecule and determining the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule; ii. expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, the target retrosynthesis model comprising a graph neural network and a reactant generation network, the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node, and the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position; iii. recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and iv. traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Claim 2. The method according to claim 1, wherein the expanding the first leaf node through a target retrosynthesis model comprises: i . determining a first string of the compound molecule corresponding to the first leaf node; ii. converting the first string into a first molecular map; iii. determining the bond breaking position of the compound molecule corresponding to the first leaf node according to the first molecular map through the graph neural network; iv. determining at least one synthon according to the bond breaking position through the reactant generation network; and v. filtering the at least one synthon based on a preset rule to determine the predicted molecule set. Claim 3. The method according to claim 2, wherein the determining the bond breaking position of the compound molecule comprises: i . determining bond breaking information according to the first molecular map and a limit on a number of broken bonds through the graph neural network; ii. determining a target key feature according to the target molecule, the target key feature comprising an atom key feature and a bond key feature, the atom key feature comprising at least one of an atom type, a number of bonds, a formal charge, chirality, a number of hydrogen atoms, an atomic hybridization state, aromaticity, an atomic weight, a high frequency reaction center feature, and a reaction type, and the bond key feature comprising at least one of a bond type, a conjugate, a ring bond, and a molecular stereo chemical feature; and iii. extracting the key breaking information based on the target key feature to determine the bond breaking position. Claim 4. The method according to claim 3, wherein the graph neural network is trained by: i . obtaining a first training molecule and a first training synthon corresponding to the first training molecule; ii. determining a node feature and an edge feature of the first training molecule and the first training synthon, the node feature being used for indicating a relationship between atoms of the first training molecule and the first training synthon, and the edge feature being used for indicating a relationship of a chemical bond between the first training molecule and the first training synthon; iii. determining a first loss function based on the node feature and the edge feature; iv. determining a bond breaking probability of the chemical bond between the first training molecule and the first training synthon; v. determining a second loss function based on the bond breaking probability; and vi. updating a model parameter of the graph neural network according to the first loss function and the second loss function. Claim 9. The method according to claim 1, wherein the recursively processing the predicted molecule set comprises: i . determining a first candidate molecule to which the predicted molecule set corresponding to the second leaf node corresponds under a preset reaction type; ii. expanding the first candidate molecule through the target retrosynthesis model to obtain third leaf nodes; iii. recursively processing the predicted molecule set corresponding to the third leaf nodes to determine a second candidate molecule; and iv. determining that a leaf node corresponding to the second candidate molecule is the terminal node in response to the second candidate molecule satisfying the preset condition. Claim 10. The method according to claim 9, wherein the determining that a leaf node corresponding to the second candidate molecule is the terminal node comprises: i . traversing based on the second candidate molecule to obtain a plurality of pathway molecules; and ii. determining that the second candidate molecule satisfies the preset condition and determining that the leaf node corresponding to the second candidate molecule is the terminal node, in response to the pathway molecule being a molecule in a basic molecule set. Claim 11. The method according to claim 9, wherein the determining that a leaf node corresponding to the second candidate molecule is the terminal node comprises: i . determining a number of expansions corresponding to the second candidate molecule in the tree structure; and ii. determining that the second candidate molecule satisfies the preset condition and determining that the leaf node corresponding to the second candidate molecule is the terminal node, in response to the number of expansions reaching a preset value. Claim 12. The method according to claim 1, i . wherein the target molecule is a drug molecule; ii. the tree structure is a Monte Carlo tree; and iii. a compound molecule corresponding to the second leaf node is obtained by screening based on at least one of functional group interference, use of a protective group, a reaction feature of a functional group, and a name reaction rule. Claim 13. An apparatus for predicting retrosynthesis of a compound molecule, comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising: i . obtaining code, configured to cause the at least one processor to obtain a target molecule and determine the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule; ii. expansion code, configured to cause the at least one processor to expand the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, the target retrosynthesis model comprising a graph neural network and a reactant generation network, the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node, and the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position; iii. processing code, configured to cause the at least one processor to recursively process the predicted molecule set corresponding to the second leaf nodes and determine a terminal node that satisfies a preset condition; and iv. prediction code, configured to cause the at least one processor to traverse path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Claim 14. The apparatus according to claim 13, wherein the expansion code is configured to cause the at least one processor to: i . determine a first string of the compound molecule corresponding to the first leaf node; ii. convert the first string into a first molecular map; iii. determine the bond breaking position of the compound molecule corresponding to the first leaf node according to the first molecular map through the graph neural network; iv. determine at least one synthon according to the bond breaking position through the reactant generation network; and v. filter the at least one synthon based on a preset rule to determine the predicted molecule set. Claim 15. The apparatus according to claim 14, wherein the program code further comprises: i . determine bond breaking code configured to cause the at least one processor to determine bond breaking information according to the first molecular map and a limit on a number of broken bonds through the graph neural network; ii. determine a target key feature according to the target molecule, the target key feature comprising an atom key feature and a bond key feature, the atom key feature comprising at least one of an atom type, a number of bonds, a formal charge, chirality, a number of hydrogen atoms, an atomic hybridization state, aromaticity, an atomic weight, a high frequency reaction center feature, and a reaction type, and the bond key feature comprising at least one of a bond type, a conjugate, a ring bond, and a molecular stereo chemical feature; and iii. key breaking extraction code configured to cause the at least one processor to extract the key breaking information based on the target key feature to determine the bond breaking position. Claim 16. The apparatus according to claim 15, wherein the graph neural network is trained by training code configured to cause the at least one processor to: i . obtain a first training molecule and a first training synthon corresponding to the first training molecule; ii. determine a node feature and an edge feature of the first training molecule and the first training synthon, the node feature being used for indicating a relationship between atoms of the first training molecule and the first training synthon, and the edge feature being used for indicating a relationship of a chemical bond between the first training molecule and the first training synthon; iii. determine a first loss function based on the node feature and the edge feature; iv. determine a bond breaking probability of the chemical bond between the first training molecule and the first training synthon; v. determine a second loss function based on the bond breaking probability; and vi. update a model parameter of the graph neural network according to the first loss function and the second loss function. Claim 20. A non-transitory computer-readable storage medium, storing a computer program that when executed by at least one processor causes the at least one processor to: i . obtain a target molecule and determining the target molecule as a root node in a tree structure, the root node being associated with a first leaf node in the tree structure, and the tree structure comprising a retrosynthetic path of the target molecule; ii. expand the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, the target retrosynthesis model comprising a graph neural network and a reactant generation network, the graph neural network being configured for predicting a bond breaking position of a compound molecule corresponding to the first leaf node, and the reactant generation network being configured for determining a predicted molecule set based on the bond breaking position; iii. recursively process the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and iv. traverse path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. Regarding Claim 1, 13, and 20, Segler et al. teach (Claim 1.i ) obtaining a target molecule as a root node in a tree structure, associated with a first leaf node in the tree structure, comprising a retrosynthetic path of the target molecule (Page 611, Column 1, Paragraph 1: Molecules are stored in the search tree as canonical SMILES strings ; Monte Carlo tree search has several desirable features, which makes it particularly well suited for retrosynthesis ; Page 606, Figure 2: Monte Carlo tree search searches by iterating over four phases ( Figure 2 shows structure of the Monte Carl Tree search for retrosynthesis including root and leaf nodes ) ). Segler et al. also teaches (Claim 1.ii ) expanding the first leaf node through a target retrosynthesis model to obtain second leaf nodes (Page 611, Column 2, Paragraph 3: During expansion, the state is processed once via the expansion procedure, and the reduced top 50 successor states are directly added to the tree ). Segler et al. also teaches (Claim 1.iii ) recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition (Page 611, Column 2, Paragraph 4: Before starting the rollout, the state is first checked for being terminal. A state can be terminal if it is solved ). Segler et al. also teaches (Claim 1.iv ) traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule (Page 611, Column 2, Paragraph 4: Terminal states are directly evaluated with the reward function ). Segler et al. also teaches the method was conducted on a computer (Page 612, Column 1, Paragraph 6: Training was carried out using stochastic gradient descent within 1–2 days on a single NVIDIA K80 graphics processing unit ). The use of a computer would indicate the presence of memory , at least one processor , and non-transitory computer readable media to carry out the method. Claims 13 and 20 recites the limitations of C laim 1. Regarding Claim 2 and 14 , Segler et al. teach (Claim 2.i ) determining a first string of the compound molecule corresponding to the first leaf node (Page 611, Column 1, Paragraph 1: Molecules are stored in the search tree as canonical SMILES strings) . Segler et al. also teach (Claim 2.ii ) converting the first string into a first molecular map (Page 611, Column 1, Paragraph 1: For processing, molecules are translated from SMILES into molecular graphs ). Segler et al. also teach (Claim 2.iv ) determining at least one synthon according to the bond breaking position through the reactant generation network (Page 605, Column 2, Paragraph 2: the transformations with the highest scores are applied to the molecule, which yields the possible precursors (precursor in this context is equivalent to synthon)). Segler et al. also teach (Claim 2.v ) filtering the at least one synthon based on a preset rule to determine the predicted molecule set (Page 605, Column 2, Paragraph 1: Prediction with the in-scope filter network; After the search space has been narrowed down by the expansion policy to the most promising transformations, we need to predict whether the corresponding reactions will actually work for a particular molecule ). Claim 14 recites the limitations of C laim 2. Regarding Claim 4 and 16 , Segler et al. teach (Claim 4.ii ) determining a node feature and an edge feature of the first training molecule and the first training synthon, the node feature being used for indicating a relationship between atoms of the first training molecule and the first training synthon, and the edge feature being used for indicating a relationship of a chemical bond between the first training molecule and the first training synthon (Page 611, Column 1, Paragraph 1: For processing, molecules are translated from SMILES into molecular graphs, which are vertex-labelled and edge-labelled graphs, with atoms as vertices and bonds as edges. Retrosynthetic transformation rules are productions on graphs ). Segler et al. also teach (Claim 4.iv ) determining a bond breaking probability of the chemical bond between the first training molecule and the first training synthon (Page 611, Column 1, Paragraph 2: A Markov decision processes is a tuple, with states (positions), actions (transformations), a transition model determining the probability of reaching state ( state indicates the molecule to be formed ) when taking action in state, and a reward function ). Claim 16 recites the limitations of claim 4. Regarding Claim 9 , Segler et al. teach (Claim 9.i ) determining a first candidate molecule to which the predicted molecule set corresponding to the second leaf node corresponds under a preset reaction type (Page 607, Column 1, Paragraph 1: Figure 3 shows an exemplary six-step route for an intermediate of a drug candidate synthesis reported in 2015, which was found by our algorithm in 5.4 s ). Segler et al. also teach (Claim 9.ii ) expanding the fi