AIMC Topic: Small Molecule Libraries
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A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.
Nature communications
Jul 1, 2025
Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligen...
Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.
Journal of chemical information and modeling
Jun 5, 2025
Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learning models have advanced drug discovery, there remains a lack of generative frameworks for covalent ...
Explainable RNA-Small Molecule Binding Affinity Prediction Based on Multiview Enhancement Learning.
Journal of chemical information and modeling
Jun 3, 2025
RNA has the potential to serve as a drug target, requiring RNA-small molecule binding affinity to screen potential drugs generally. However, accurately predicting RNA-small molecule binding affinity remains a highly challenging task. This study propo...
In Silico Screening of Small Molecule Inhibitors for Amyloid-β Aggregation.
Journal of chemical information and modeling
May 29, 2025
The self-aggregation of amyloid-β (Aβ) into fibrils is a hallmark of Alzheimer's disease (AD). Inhibition of Aβ aggregation with small molecule compounds represents a promising therapeutic strategy for AD. However, designing effective ligands is chal...
The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning.
International journal of biological macromolecules
Apr 21, 2025
Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the ...
Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model.
Journal of chemical information and modeling
Mar 28, 2025
Accurate retention time (RT) prediction in liquid chromatography remains a significant consideration in molecular analysis. In this study, we explore the use of a transformer-based language model to predict RTs by treating simplified molecular input ...
Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.
Journal of chemical information and modeling
Mar 24, 2025
A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 proteins in a typical human cell offer ...
Rapid traversal of vast chemical space using machine learning-guided docking screens.
Nature computational science
Mar 13, 2025
The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for ...
A dataset for machine learning-based QSAR models establishment to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library.
BMC research notes
Mar 3, 2025
OBJECTIVES: Beta-lactamase is a bacterial enzyme that deactivates beta-lactam antibiotics, and it is one of the leading causes of antibiotic resistance problems globally. In current drug discovery research, molecular simulation, like molecular dockin...
Machine Learning in Small-Molecule Mass Spectrometry.
Annual review of analytical chemistry (Palo Alto, Calif.)
Feb 27, 2025
Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis; however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass s...
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