| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jcheminf/GenhedenTCREB20 |
| P356 | DOI | 10.1186/S13321-020-00472-1 |
| P932 | PMC publication ID | 7672904 |
| P698 | PubMed publication ID | 33292482 |
| P50 | author | Jean-Louis Reymond | Q42719788 |
| Ola Engkvist | Q43370883 | ||
| Esben J Bjerrum | Q64684836 | ||
| P2093 | author name string | Amol Thakkar | |
| Samuel Genheden | |||
| Veronika Chadimová | |||
| P2860 | cites work | InChI, the IUPAC International Chemical Identifier | Q21146620 |
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| Machine Learning in Computer-Aided Synthesis Planning | Q53827243 | ||
| A Survey of Monte Carlo Tree Search Methods | Q57382679 | ||
| Computer-Assisted Design of Complex Organic Syntheses | Q61779901 | ||
| A robotic platform for flow synthesis of organic compounds informed by AI planning | Q67654159 | ||
| Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain | Q89941104 | ||
| Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction | Q90403932 | ||
| A retrosynthetic analysis algorithm implementation | Q90858643 | ||
| The Synthesizability of Molecules Proposed by Generative Models | Q91590035 | ||
| Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks | Q91876087 | ||
| RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application | Q92694883 | ||
| Synthetic Approaches to the New Drugs Approved During 2017 | Q92786018 | ||
| "Ring Breaker": Neural Network Driven Synthesis Prediction of the Ring System Chemical Space | Q94466387 | ||
| P4510 | describes a project that uses | Jupyter notebook file | Q70357595 |
| P433 | issue | 1 | |
| P921 | main subject | open-source software | Q1130645 |
| P304 | page(s) | 70 | |
| P577 | publication date | 2020-11-17 | |
| P1433 | published in | Journal of Cheminformatics | Q6294930 |
| P1476 | title | AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning | |
| P478 | volume | 12 |
| Q108127160 | Artificial intelligence to deep learning: machine intelligence approach for drug discovery |
| Q116181392 | Automation and computer-assisted planning for chemical synthesis |
| Q125904506 | Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back |
| Q129269660 | De novo drug design through artificial intelligence: an introduction |
| Q125767447 | Deep learning for metabolic pathway design |
| Q123528026 | Dual use of artificial-intelligence-powered drug discovery |
| Q104412579 | From Big Data to Artificial Intelligence: chemoinformatics meets new challenges |
| Q111792924 | LEADD: Lamarckian evolutionary algorithm for de novo drug design |
| Q113204816 | Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph |
| Q108812232 | Prediction of chemical reaction yields using deep learning |
| Q128772762 | Retro-BLEU: quantifying chemical plausibility of retrosynthesis routes through reaction template sequence analysis |
| Q115010774 | SynthI: A New Open-Source Tool for Synthon-Based Library Design |
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