ReviewMethods in molecular biology (Clifton, N.J.)2022
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.
Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.Current drug discovery technologies · 2026Review
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Thirty Years of Structural Studies on Cyclin-Dependent Kinases: Implications for Drug Discovery.Current medicinal chemistry · 2025Article
- Protein-Ligand Docking Simulations for Drug Discovery.Current medicinal chemistry · 2025Article
- AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.Journal of cheminformatics · 2024Article
- Computational Approaches and Drug Discovery: Where Are We Going?Molecules (Basel, Switzerland) · 2024Article
- Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.Journal of cheminformatics · 2024Article
- Accelerating COVID-19 Drug Discovery with High-Performance Computing.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Molecular Dynamics and Other HPC Simulations for Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- AlphaFold2 protein structure prediction: Implications for drug discovery.Current opinion in structural biology · 2023Review
- On the importance for drug discovery of a transnational Latin American database of natural compound structures.Frontiers in pharmacology · 2023Article
- Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models.Molecules (Basel, Switzerland) · 2022Article
- A toolkit for covalent docking with GOLD: from automated ligand preparation with KNIME to bound protein-ligand complexes.Bioinformatics advances · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.
Indexed as
Identifiers
34731478What 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.