AIMC Topic: Small Molecule Libraries

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The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning.

Computational biology and chemistry
Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. Wit...

Deep Learning of CYP450 Binding of Small Molecules by Quantum Information.

Journal of chemical information and modeling
Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the me...

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

Journal of chemical information and modeling
The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While Pfizer introduced , an FDA-approved antiviral targeting the SARS-CoV-2 main protease (Mpro), this st...

MDFGNN-SMMA: prediction of potential small molecule-miRNA associations based on multi-source data fusion and graph neural networks.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experi...

Progress of machine learning in the application of small molecule druggability prediction.

European journal of medicinal chemistry
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and...

Coverage bias in small molecule machine learning.

Nature communications
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end mo...

Interpretable Deep-Learning p Prediction for Small Molecule Drugs via Atomic Sensitivity Analysis.

Journal of chemical information and modeling
Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to nove...

Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.

European journal of medicinal chemistry
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...

CPIScore: A Deep Learning Approach for Rapid Scoring and Interpretation of Protein-Ligand Binding Interactions.

Journal of chemical information and modeling
Protein-ligand binding affinity prediction is a crucial and challenging task in the field of drug discovery. However, traditional simulation-based computational approaches are often prohibitively time-consuming, limiting their practical utility. In t...

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.

International journal of molecular sciences
The enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compoun...