ArticleBriefings in bioinformatics2024
DrugMetric: quantitative drug-likeness scoring based on chemical space distance.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- TRIZ-guided computational innovation framework for the automated generation and prioritization of novel azole-like antifungal candidates targeting Candida auris CYP51.Journal of computer-aided molecular design · 2026Article
- Integrated In Silico Prioritization of Antidiabetic Phytochemicals fromMolecules (Basel, Switzerland) · 2026Article
- The Effect of Viniferin on Liver Cancer: Research Based on Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.Medical sciences (Basel, Switzerland) · 2026Article
- Insights into Bioactive Constituents from Pericarp ofAntioxidants (Basel, Switzerland) · 2026Article
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- Identification of two terpenoids from Withania coagulans with predicted multitarget binding affinity: An in vitro and in silico study.PloS one · 2026Article
- RL-MSVP: Reinforcement Learning and Multi-scale Validation Pipeline Investigating Azole-Fused Pyrimidines as PotentialACS omega · 2025Article
- Discovering the Anti-Inflammatory Potential of Compounds Isolated from the Aerial Parts ofMolecules (Basel, Switzerland) · 2025Article
- Integrated molecular and ADME-toxicity profiling identifies PGV-5 and HGV-5 as potential agents to counteract multidrug-resistant (MDR) cancer.Scientific reports · 2025Article
- Development of PROTAC-Based Strategies for Cannabinoid Receptor Type 1 (CBACS pharmacology & translational science · 2025Article
- Article
- DyeLeS: a web platform for predicting and classifying fluorescence properties of bioactive molecules.RSC advances · 2025Article
- DrugGen enhances drug discovery with large language models and reinforcement learning.Scientific reports · 2025Article
- The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom's law.Drug discovery today · 2025Review
- Gingerols and Shogaols ofJournal of experimental pharmacology · 2025Article
- Rational design of allosteric inhibitors targeting C797S mutant EGFR in NSCLC: an integrative in silico andFrontiers in oncology · 2025Article
- Article
- Family Malvaceae: a potential source of secondary metabolites with chemopreventive and anticancer activities supported withFrontiers in pharmacology · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.
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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.