7 papers
A Modular Agentic Framework for Synthetically Constrained Multi-Objective Hit-to-Lead Optimization
Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland +6
Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We pre…
BASIL: Bayesian Application for Scientific Iteration and Learning
Kelvin P. Idanwekhai, Valeriia Kaneva, Stefano Menegatti +1
We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition functions that can be used to…
Extending machine learning model for implicit solvation to free energy calculations
Rishabh Dey, Michael Brocidiacono, Kushal Koirala +2
The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to…
KANEL: Kolmogorov-Arnold Network Ensemble Learning Enables Early Hit Enrichment in High-Throughput Virtual Screening
Pavel Koptev, Nikita Krainov, Konstantin Malkov +1
Machine learning models of chemical bioactivity are increasingly used for prioritizing a small number of compounds in virtual screening libraries for experimental follow-up. In the…
Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matching
Yunni Qu, Bhargav Vaduri, Karthikeya Jatoth +6
Machine learning (ML) models are increasingly deployed for virtual screening in drug discovery, where the goal is to identify novel, chemically diverse scaffolds while minimizing e…
Binding Free Energies without Alchemy
Michael Brocidiacono, Brandon Novy, Rishabh Dey +2
Absolute Binding Free Energy (ABFE) methods are among the most accurate computational techniques for predicting protein-ligand binding affinities, but their utility is limited by t…