M. Islam

Free energy perturbation and machine learning-assisted identification of novel molecules for the Mycobacterium tuberculosis KasA protein: a fragment-based drug design approach.

SilicoXplore: An integrated cloud platform coupling machine learning with physics-based modelling for end-to-end drug discovery, applied to the computational prioritisation of putative New Delhi metallo-β-lactamase-1 binders.

Modern drug discovery joins machine learning with physics-based simulation, but building such a pipeline needs Linux administration, dependency management, format conversion and scripting, which keeps out many of the chemists and biologists who ask the questions. We describe SilicoXplore, a cloud-hosted platform of 31...

Artificial Intelligence‐Guided Design and Computational Evaluation of Candidate CYP51B Inhibitors as Potential Antifungal Agents

CYP51B (sterol 14α‐demethylase) is essential for ergosterol biosynthesis in Aspergillus fumigatus and is the target of the clinical azole antifungals. A curated library of 174 antifungal agents was expanded with the Mol2Mol module of REINVENT4 to 111,655 unique structures, of which 60,120 survived diversity filteri...

Free energy perturbation and machine learning-assisted identification of potential MAP3K8 hit molecules: a comprehensive structure- and ligand-based studies.

The convergence of docking, dynamics, and free-energy results prioritized PM2, PM3, and PM4 as promising MAP3K8 hit candidates, which require further experimental validation and lead optimization.