Machine learning-guided screening and validation of antioxidant small molecules from Clausena lansium

Consensus ensemble learning with augmented molecular representation for accelerated virtual screening of kelulut honey phytochemicals targeting histamine H1 receptor

An H1R-specific ensemble model for predicting binding affinity scores between compounds extracted from Malaysian Kelulut Honey and H1R is developed, underscoring its potential to accelerate and optimize the effectiveness of drug development across various diseases.

Machine Learning–Driven Discovery of Dietary Polyphenol DPP-4 Inhibitors via Molecular Docking

This study aimed to identify polyphenolic compounds derived from dietary sources as potential DPP-4 inhibitors through an in silico pipeline integrating machine learning (ML) and molecular docking.

In silico screening of anti-atherosclerotic compounds from Morus alba leaves by machine learning and network pharmacology.

OBJECTIVE This study integrates machine learning with network pharmacology, molecular docking, and molecular dynamics simulations to screen bioactive compounds from Mulberry leaves and elucidate their potential mechanisms against atherosclerosis (AS). METHODS A training dataset of anti-AS active compounds was compile...

Machine Learning Integrated Designing and Screening of 8-Hydroxyquinoline Based Metallo-β-Lactamase Inhibitors

The rapid emergence of metallo-b-lactamase-mediated antibiotic resistance has created an urgent need for new inhibitor discovery strategies. In this work, a machine-learning-guided workflow was developed to generate and prioritize potential inhibitors targeting NDM-1. A SMILES-based variational autoencoder was first pr...

Exploration and Screening of Database of Polycyclic Aromatic Hydrocarbons to Find Compounds with Lower Enthalpy of Formation

Chemoinformatic prioritization of bioactive compounds associated with the bread matrix based on ChEMBL pharmacological annotations and machine learning

The objective was to develop a reproducible chemoinformatics framework for internal prioritization of compounds associated with bread and cereal matrices based on structural features and ChEMBL pharmacological annotations based on structural features and ChEMBL pharmacological annotations.