ReviewInternational journal of molecular sciences2023
Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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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.
The trial behind it
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Who cites it
44 citing papers in PubMed.
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- Mixture-Quantitative Structure-Property Relationship Approaches for Jet Fuel-Component Blends: A Review.Molecular informatics · 2026Review
- GSF-DTA: An Innovative Graph-Sequence Fusion Framework for Drug-Target Affinity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Artificial intelligence and peripheral neuropathies: Strategies for the development, application, and repair of regenerative biomaterials.Neural regeneration research · 2026Article
- NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction.Journal of chemical information and modeling · 2026Article
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Efficient and valid large molecule generation via self-supervised generative models.npj drug discovery · 2026Article
- Prediction of physicochemical properties of organic compounds using degree-based topological indices and machine learning models.Scientific reports · 2026Article
- Machine Learning-Driven QSAR Modeling of FXIa Inhibitors for Virtual Screening and Rational Drug Design.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular Fingerprints.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Machine Learning-Based QSAR Models for Discovery of Inhibitors TargetingPharmaceuticals (Basel, Switzerland) · 2026Article
- AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.Molecular diversity · 2026Article
- Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.International journal of molecular sciences · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- Task-specific pre-training for molecular property prediction.Briefings in bioinformatics · 2026Article
- Structure-activity modeling and hybrid machine learning-based prediction of bioactivity in pyrazole derivatives for drug discovery applications.Turkish journal of biology = Turk biyoloji dergisi · 2026Article
- Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations.PloS one · 2026Article
- Predictive modeling and ranking of physicochemicals inFrontiers in chemistry · 2026Article
- An integrative computational approach for identification of NLRP3 inhibitors through machine learning, docking, dynamics and DFT analysis.Scientific reports · 2025Article
- Leveraging Molecular Mechanics With the uESE Continuum Solvation Model for Efficient Solvation Free Energy Prediction: Impact of Conformation and Extensive Validation.Journal of computational chemistry · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.
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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.