FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning.

Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.

Discovery of a Potent and Selective Tyrosine Kinase 2 Inhibitor: TAK-279.

TYK2 is a key mediator of IL12, IL23, and type I interferon signaling, and these cytokines have been implicated in the pathogenesis of multiple inflammatory and autoimmune diseases such as psoriasis, rheumatoid arthritis, lupus, and inflammatory bowel diseases. Supported by compelling data from human genome-wide association studies and clinical results, TYK2 inhibition through small molecules is an attractive therapeutic strategy to treat these diseases. Herein, we report the discovery of a series of highly selective pseudokinase (Janus homology 2, JH2) domain inhibitors of TYK2 enzymatic activity. A computationally enabled design strategy, including the use of FEP+, was instrumental in identifying a pyrazolo-pyrimidine core. We highlight the utility of computational physics-based predictions used to optimize this series of molecules to identify the development candidate 30, a potent, exquisitely selective cellular TYK2 inhibitor that is currently in Phase 2 clinical trials for the treatment of psoriasis and psoriatic arthritis.

Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations.

Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.

Proton solvation and transport in hydrated nafion.

Proton solvation properties and transport mechanisms have been studied in hydrated Nafion using the self-consistent multistate empirical valence bond (SCI-MS-EVB) method that includes the effects excess proton charge defect delocalization and Grotthuss proton hopping. It was found that sulfonate groups influence excess proton solvation, as well as the proton hydration structure, by stabilizing a more Zundel-like (H(5)O(2)(+)) structure in their first solvation shells. Hydrate proton-related hydrogen bond networks were observed to be more stable than networks with water alone. Diffusion rates, Arrhenius activation energies, and transport pathways were calculated and analyzed to characterize the nature of the proton transport. Diffusion rate analysis suggests that a proton-hopping mechanism dominates the proton transport for the studied water loading levels and that there is a clear degree of anticorrelation with the vehicular transport. The activation energy drops quickly with an increasing water content when the water loading level is smaller than ∼10 H(2)O/SO(3)(-), which is consistent with experimental observations. The sulfonate groups were also found to affect the proton hopping directions. The temperature and water content effects on the proton transport pathways were also investigated.

Transferable Coarse-Grained Models for Ionic Liquids.

The effective force coarse-graining (EF-CG) method was applied to the imidazolium-based nitrate ionic liquids with various alkyl side-chain lengths. The nonbonded EF-CG forces for the ionic liquid with a short side chain were extended to generate the nonbonded forces for the ionic liquids with longer side chains. The EF-CG force fields for the ionic liquids exhibit very good transferability between different systems at various temperatures and are suitable for investigating the mesoscopic structural properties of this class of ionic liquids. The good additivity and ease of manipulation of the EF-CG force fields can allow for an inverse design methodology of ionic liquids at the coarse-grained level. With the EF-CG force field, the molecular dynamics (MD) simulation at a very large scale has been performed to check the significance of finite size effects on the structural properties. From these MD simulation results, it can be concluded that the finite size effect on the phenomenon of ionic liquid spatial heterogeneity (Wang, Y.; Voth, G. A. J. Am. Chem. Soc. 2005, 127, 12192) is small and that this phenomenon is indeed a nanostructural behavior which leads to the experimentally observed mesoscopic heterogeneous structure of ionic liquids.

Quantum instanton evaluation of the thermal rate constants and kinetic isotope effects for SiH4+H-->SiH3+H2 reaction in full Cartesian space.

The quantum instanton calculations of thermal rate constants for the gas-phase reaction SiH4+H-->SiH3+H2 and its deuterated analogs are presented, using an analytical potential energy surface. The quantum instanton approximation is manipulated by full dimensionality in Cartesian coordinate path integral Monte Carlo approach, thereby taking explicitly into account the effects of the whole rotation, the vibrotational coupling, and anharmonicity of the reaction system. The rates and kinetic isotope effects obtained for the temperature range of 200-1000 K show good agreements with available experimental data, which give support to the accuracy of the underlying potential surface used. In order to investigate the sole quantum effect to the rates, the authors also derive the classical limit of the quantum instanton and find that it can be exactly expressed as the classical variation transition state theory. Comparing the quantum quantities with their classical analogs in the quantum instanton formula, the authors demonstrate that the quantum correction of the prefactor is more important than that of the activation energy at the transition state.