mlsanityData to 7 Oct 2026

Learning to Discover Medicines

arXiv 2202.07096 ↗First posted 14 Feb 2022cs.AIcited by 0

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Abstract (arXiv)

Discovering new medicines is the hallmark of human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 138 references that are in the map. First posted on arXiv 14 Feb 2022.

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44 of 138 references.

References · 44

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Graph Attention Networkscited by 8,854
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  8. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  9. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  10. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  11. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
  12. 2018GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelscited by 406
32 more
  1. 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 375
  2. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  3. 2018Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generationcited by 363
  4. 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 332
  5. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 318
  6. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  7. 2021Graph Contrastive Learning Automatedcited by 287
  8. 2017Grammar Variational Autoencodercited by 283
  9. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 265
  10. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  11. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  12. 2018DeepDTA: Deep Drug-Target Binding Affinity Predictioncited by 171
  13. 2017Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learningcited by 161
  14. 2020Retrosynthesis Prediction with Conditional Graph Logic Networkcited by 103
  15. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  16. 2016Low Data Drug Discovery with One-shot Learningcited by 91
  17. 2020A Graph to Graphs Framework for Retrosynthesis Predictioncited by 85
  18. 2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
  19. 2019Mol-CycleGAN - a generative model for molecular optimizationcited by 66
  20. 2020MolTrans: Molecular Interaction Transformer for Drug Target Interaction Predictioncited by 63
  21. 2018Graph Transformation Policy Network for Chemical Reaction Predictioncited by 56
  22. 2017Development and evaluation of a deep learning model for protein-ligand binding affinity predictioncited by 50
  23. 2021The AI Index 2021 Annual Reportcited by 34
  24. 2016Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Modelcited by 31
  25. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  26. 2020HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognitioncited by 26
  27. 2021Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphscited by 25
  28. 2017Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinitycited by 21
  29. 2020GEFA: Early Fusion Approach in Drug-Target Affinity Predictioncited by 16
  30. 2021Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphscited by 7
  31. 2018Graph Memory Networks for Molecular Activity Predictioncited by 4
  32. 2018Attentional Multilabel Learning over Graphs: A Message Passing Approachcited by 1

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