Next-generation integrated quantum force fields for biomedical applications

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Next-generation integrated quantum force fields for biomedical applications PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA. We have recently developed novel framework for next-generation quantum mechanical force fields (QMFFs) designed to meet the challenges of biomolecular simulations and drug discovery applications. QMFFs have tremendous computational advantages relative to their fully QM counterparts, being inherently parallelizable and linearly scaling, offering tremendous computational speedup, and promising quantitative accuracy potentially superior to full QM methods. QMFFs accurately model multipolar electrostatics, charge penetration effects, and non-linear polarization response. QMFFs thus offer a transformative technology for drug discovery applications, in particular, for advancing the predictive capability of free energy simulations in lead refinement. These are critically important for the diverse chemical space of drug molecules, including halogen bonding, cation- and metal-ligand interactions. Further, QMFFs offer a mechanism for modeling covalent inhibitors. Specifically, we propose to: I. Develop new QMFFs for drug discovery. QMFFs will be developed based on both semiempirical and ab initio density-functional methods in the following stages: 1) determination of multipolar mapping parameters enhancing the DFTB electrostatic potential to reach greater accuracy, 2) augmentation of electronic response terms using chemical potential equalization (CPE) corrections using an orthogonal perturbation-response approach to solve the under-polarization problem of DFTB methods, 3) parameterization of non-electrostatic non-bonded interac- tion parameters using realistic potentials that capture many-body exchange and dispersion interactions, and 4) exploration of statistical potentials, using machine learning approaches applied to quantum data sets, to correct internal conformational energies and short-range interactions. II. Develop new free energy methods to enable protein-ligand binding predictions using QMFFs. We will develop a novel integrated free energy pipeline to pre- dict alchemical binding free energies for ligands and inhibitors. This will include new GPU-accelerated methods for -space self-adaptive mixture sampling ( -SAMS) and 2D-vFEP analysis, coupled with conformational space enhanced sampling methods for alchemical steps of the thermodynamic cycle, and advancements in free en- ergy “book-ending” methods (BBQm) to efficiently connect molecular mechanical force field and QMFF model representations. III. Test and validate QMFFs and free energy methods, and apply to MIF inhibitor binding. The methods will be broadly tested against established data sets for solvation free energies, and a drug discovery data set. More in-depth validation studies will be conducted by examining the relative binding free energies of inhibitors of the macrophage inhibitory factor (MIF). Finally, exploratory applications will examine mechanisms, characterize transition states and predict rates for covalent inhibition for a series of MIF inhibitors.