ReviewJournal of cheminformatics2020
Molecular representations in AI-driven drug discovery: a review and practical guide.
Review in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
167 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis and review of in silico methods in drug discovery - part 1: technological evolution and trends from big data to chemical space.The pharmacogenomics journal · 2025Pooled it
- A molecular modeling and machine learning-based approach for the identification of potential human ALOX15 inhibitors.Journal of molecular modeling · 2026Article
- MacroTox: A Macroscopic Graph Topology-Based Multimodal Learning Framework for Robust Molecular Toxicity Prediction.JACS Au · 2026Article
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Is AI Capable of Real-World Drug Discovery?Journal of chemical information and modeling · 2026Article
- Quantum-Trained AI Enables Inverse Design of Organic Frontier Orbitals at Billion-Scale.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Targeting WEE1 kinase: an integrated machine learning-cheminformatics framework for ultra-large-scale virtual screening and novel inhibitor discovery.Journal of computer-aided molecular design · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.Molecular diversity · 2026Article
- Artificial intelligence in pharmaceutics: Why prediction without formulation is incomplete.Asian journal of pharmaceutical sciences · 2026Article
- High-throughput solid microsampling through stochastic robotic automation.Communications chemistry · 2026Article
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- A foundation model enables prediction of natural product molecular properties, bioactivity, and structural similarity from biosynthetic gene cluster sequence.bioRxiv : the preprint server for biology · 2026Article
- Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.Journal of cheminformatics · 2026Article
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Undersampling Techniques for Nonlinear Chemical Space Visualization.Molecular informatics · 2026Article
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- A Bond-Level Sequence Framework for Molecular Representation Learning with Structural Constraints.Molecules (Basel, Switzerland) · 2026Article
- Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning.Scientific reports · 2026Article
107 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.
Indexed as
Identifiers
What OpenQuestion holds
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.