Next-generation alchemical free energy methods and quantum/machine-learning models for drug discovery

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Next-generation alchemical free energy methods and quantum/machine-learning models for drug discovery. PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA. Alchemical free energy (AFE) simulations are indispensable in various aspects of drug discovery by enabling the prediction of ligand binding affinity and selectivity. A critical barrier to progress is the current limitation in pre- cision and accuracy of AFE simulations that restricts their predictive capability. The current proposal addresses these barriers with new AFE methods and models that will be integrated into the GPU-accelerated AMBER soft- ware suite used worldwide (over 30K users) in academia, government labs and industry. Specifically, we propose to: 1. Create advanced technology for robust high-precision AFE simulations; 2. Develop accurate quantum mechanical/deep-learning potential (QDπ) force fields for drug discovery and 3. Validate precision and accu- racy of AFE methods and QDπ model. In Aim 1, we will develop new technologies for robust and reproducible calculation of ligand-protein binding free energies of compound libraries. The methods work together to enable highly precise, converged AFE simulations across thermodynamic graph networks. In Aim 2, we will develop a highly accurate and computationally efficient general quantum deep-potential interaction (QDπ) force field model for drug discovery. The QDπ model will be formulated as a machine learning potential correction (∆-MLP) to the quantum mechanical/molecular mechanical (QM/MM) energy using fast, approximate 3rd-order density-functional tight-binding QM model and well-established AMBER MM force fields and compatible water and ions models. The ∆-MLP will leverage our recently developed range-corrected deep-learning potential (DPRc) for accurate intra- and intermolecular interactions. In Aim 3, we will conduct in depth validation studies of the AFE methods from Aim 1 and QDπ model of Aim 2 on a systematic set of benchmark systems, including macrophage migration inhibitory factor (MIF), JAK2 JH2 domain, SARS-Cov2 Mpro, and sigma 1 and 2 receptors.