mlsanityData to 7 Oct 2026

Artificial Intelligence for Drug Discovery: Are We There Yet?

arXiv 2307.06521 ↗First posted 13 Jul 2023cs.AI cs.LG q-bio.QMcited by 11

Not on the map. Why?

Abstract (arXiv)

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 13 of its 181 references that are in the map. First posted on arXiv 13 Jul 2023.

Builds on

13 of 181 references.

References · 13

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  3. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  4. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  5. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
  6. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  7. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  8. 2022Leakage and the Reproducibility Crisis in ML-based Sciencecited by 63
  9. 2022Neural-Symbolic Models for Logical Queries on Knowledge Graphscited by 60
  10. 2022TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoverycited by 45
  11. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
  12. 2020Differences between human and machine perception in medical diagnosiscited by 13
1 more
  1. 2022MolE: a molecular foundation model for drug discoverycited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 1.

11 papers.

  1. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  2. 2025MolVision: Molecular Property Prediction with Vision Language Modelscited by 5
  3. 2023Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Predictioncited by 4
  4. 2024A Survey on Large Language Models from Concept to Implementationcited by 4
  5. 2025Equivariant Masked Position Prediction for Efficient Molecular Representationcited by 4
  6. 2024Preference-based Pure Explorationcited by 3
  7. 2024Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Predictioncited by 1
  8. 2025Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Trainingcited by 1
  9. 2026TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacologycited by 1
  10. 2025Flow-Based Fragment Identification via Binding Site-Specific Latent Representationscited by 0
  11. 2025Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integrationcited by 0