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physics.chem-ph2026
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…
physics.chem-ph2025
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…