AIMC Topic: Small Molecule Libraries

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Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.

Molecular diversity
MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potentia...

DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.

Nucleic acids research
DeepMolecules is an easily accessible web server for predicting protein-small molecule interactions. It integrates four state-of-the-art models: ESP and SPOT for identifying substrates of enzymes and transporters, respectively, TurNuP for predicting ...

Computational Hit Finding: An Industry Perspective.

Journal of medicinal chemistry
Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converg...

Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.

Journal of chemical theory and computation
The extensive conformational dynamics of partially disordered proteins hinders the efficiency of traditional in-silico structure-based drug discovery approaches due to the challenge of screening large chemical spaces of compounds, albeit with an exce...

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks.

Journal of chemical information and modeling
This study introduces PROFIS, a new generative model capable of the design of structurally novel and target-focused compound libraries. The model relies on a recurrent neural network that was trained to decode embedded molecular fingerprints into SMI...

A Database for Large-Scale Docking and Experimental Results.

Journal of chemical information and modeling
The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully sha...

Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein.

Scientific reports
The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assiste...

BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data.

Nucleic acids research
BindingDB (bindingdb.org) is a public, web-accessible database of experimentally measured binding affinities between small molecules and proteins, which supports diverse applications including medicinal chemistry, biochemical pathway annotation, trai...

DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computationa...

DGCL: dual-graph neural networks contrastive learning for molecular property prediction.

Briefings in bioinformatics
In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraini...