Chemical Space Exploration with Active Learning and Alchemical Free Energies.

Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries.

Alchemical absolute protein-ligand binding free energies for drug design.

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets.

Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches.

The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space.

Non-equilibrium approach for binding free energies in cyclodextrins in SAMPL7: force fields and software.

In the current work we report on our participation in the SAMPL7 challenge calculating absolute free energies of the host-guest systems, where 2 guest molecules were probed against 9 hosts-cyclodextrin and its derivatives. Our submission was based on the non-equilibrium free energy calculation protocol utilizing an averaged consensus result from two force fields (GAFF and CGenFF). The submitted prediction achieved accuracy of [Formula: see text] in terms of the unsigned error averaged over the whole dataset.

Improved general-purpose five-point model for water: TIP5P/2018.

A new five point potential for liquid water, TIP5P/2018, is presented along with the techniques used to derive its charges from per-molecule electrostatic potentials in the liquid phase using the split charge equilibration of Nistor [J. Chem. Phys.