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.

Electronic Excitations in Complex Molecular Environments: Many-Body Green's Functions Theory in VOTCA-XTP.

Many-body Green's functions theory within the GW approximation and the Bethe-Salpeter Equation (BSE) is implemented in the open-source VOTCA-XTP software, aiming at the calculation of electronically excited states in complex molecular environments. Based on Gaussian-type atomic orbitals and making use of resolution of identity techniques, the code is designed specifically for nonperiodic systems. Application to a small molecule reference set successfully validates the methodology and its implementation for a variety of excitation types covering an energy range from 2 to 8 eV in single molecules.

Non-conformal coarse-grained potentials for water.

Water is a notoriously difficult substance to model both accurately and efficiently. Here, we focus on descriptions with a single coarse-grained particle per molecule using the so-called approximate non-conformal and generalized Stockmayer potentials as the starting points. They are fitted using the radial distribution function and the liquid-gas density profile of the atomistic extended simple point charge (SPC/E) model by downhill simplex optimization.

Redefining the minimal substrate tolerance of mandelate racemase. Racemization of trifluorolactate.

Mandelate racemase (EC 5.1.2.