# Artificial Intelligence for Drug Discovery: Are We There Yet?

arXiv: https://arxiv.org/abs/2307.06521 · HTML: https://mlsanity.com/paper/2307.06521/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 181 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Pre-training Molecular Graph Representation with 3D Geometry](https://mlsanity.com/paper/2110.07728/) (2021)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [Neural-Symbolic Models for Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/2205.10128/) (2022)
- [TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery](https://mlsanity.com/paper/2202.08320/) (2022)
- [Computer-Aided Multi-Objective Optimization in Small Molecule Discovery](https://mlsanity.com/paper/2210.07209/) (2022)
- [Differences between human and machine perception in medical diagnosis](https://mlsanity.com/paper/2011.14036/) (2020)
- [MolE: a molecular foundation model for drug discovery](https://mlsanity.com/paper/2211.02657/) (2022)

## Built on by (most cited first, 11 of 11)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [Preference-based Pure Exploration](https://mlsanity.com/paper/2412.02988/) (2024)
- [MolVision: Molecular Property Prediction with Vision Language Models](https://mlsanity.com/paper/2507.03283/) (2025)
- [Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction](https://mlsanity.com/paper/2312.17495/) (2023)
- [A Survey on Large Language Models from Concept to Implementation](https://mlsanity.com/paper/2403.18969/) (2024)
- [Equivariant Masked Position Prediction for Efficient Molecular Representation](https://mlsanity.com/paper/2502.08209/) (2025)
- [Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction](https://mlsanity.com/paper/2408.00421/) (2024)
- [Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training](https://mlsanity.com/paper/2511.04040/) (2025)
- [TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology](https://mlsanity.com/paper/2606.19245/) (2026)
- [Flow-Based Fragment Identification via Binding Site-Specific Latent Representations](https://mlsanity.com/paper/2509.13216/) (2025)
- [Unified Molecule Pre-training with Flexible 2D and 3D Modalities: Single and Paired Modality Integration](https://mlsanity.com/paper/2510.07035/) (2025)

## 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.