ArticleBriefings in bioinformatics2023
From intuition to AI: evolution of small molecule representations in drug discovery.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Article
- Classification of Thyroid Peroxidase (TPO) Inhibitors Using Transfer Learning with SMILES Embeddings.Chemical research in toxicology · 2026Article
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.Scientific reports · 2026Article
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.Briefings in bioinformatics · 2026Article
- Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research.The Journal of organic chemistry · 2026Review
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings.GigaScience · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- ProtoMol: enhancing molecular property prediction via prototype-guided multimodal learning.Briefings in bioinformatics · 2025Article
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- Discovery of Novel c‑MET Inhibitors for Hepatocellular Carcinoma Using an Integrated Virtual Screening Approach.ACS medicinal chemistry letters · 2025Article
- Generalizable, fast, and accurate DeepQSPR with fastprop.Journal of cheminformatics · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- Discovery of Novel DDR1 Inhibitors through a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulations.ACS medicinal chemistry letters · 2025Article
- Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches.Molecules (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.