Biography
Prof. Zheng Zheng
Prof. Zheng Zheng
Wuhan University of Technology, China
Title: Applying the Bayesian Field Theory and Deep Learning for Modeling the Multi-body Energy in Proteins
Abstract: 
A central task for computational chemistry is to accurately and efficiently simulate the molecular free energy, which providestheoretical proof and quantitative support for many study fields, e.g. structure-based drug design, protein engineering, etc. To achieve that goal requires reliable molecular potential energy models constituting a good balance between computational accuracy and operational simplicity. Quantum mechanics methods achieve high prediction accuracy while are extremely expensive against complicated biomolecular systems. Force fields have long been the most widely used potential energy models for molecular free energy simulations, for their well-trained parameter set and easy-to-compute functional forms. However, the single-atom or fragment based pairwise potential functions are facing inherent difficulties for simulating quantum level phenomena caused by the many body effects e.g. electric polarizability and charge transfer, bringing the development of force fields to a bottleneck. Inspired by the ideas from the Graph theory and machine learning techniques, we hereby propose a new protocol for the molecular potential energy model generation by using the Bayesian Field Theory (BFT) and Artificial Neural Network (ANN) based machine learning. This project aims to introduce the ANN-based potential model, for protein systems and protein-ligand complex systems, not only using the atom pairwise potentials, but also including the multi-dimensional configurational effects. In this project, we plan to introduce BFT to set up isolated close-ranged many body systems centered at every atom under study within a molecule, so that the environmental structural configurations regarding each atom descriptors can be tracked across all the molecules in the training database. We then plan to use the deep learning method for training a multi-layered ANN as the biomolecular energy model against massive number ofhigh-level QM method calculated molecular single point energies together with high-quality molecular global minimum structures. This project will provide new insight for understanding the benefit of using machine learning methods to simulate and interpret the complicated configurational effects beyond the illustration of molecular mechanics, without the need to employ high-cost quantum level computation. Finally, this project plan to embed this new ANN-based potential model in the commercialized “Movable Type” free energy method initially developed by the applicant, to achieve both high speed and high accuracy in the free energy simulation for biomolecular systems.
Biography: 

Major Achievements

Invention and development of one novel free energy simulation method applied for rational drug design and protein engineering, named “Movable-Type” method, with 1 international patent publishedin 2013. A software package, DivCon Discovery Suite-Movable Type Package, has been developed based on this patent while commercialized and distributed by QuantumBio Inc., including (1) MT-CS, a small biomolecule conformational search program and (2) MT-Heatmap Docking, a protein-ligand docking and scoring program.(www.quantumbioin.com/support/manual/movabletype)

Invention and development of three molecular potential energy functions (LISA, LISA+ and KECSA) designed for use with the “Movable-Type” method and other docking and scoring procedures.

Over 20 published papers in top-ranking publications including Nature Chemistry, Journal of Chemical Theory and Computation, Journal of Chemical Information and Modeling, et al. See citation indices at https://scholar.google.com/citations?user=OfPXgP0AAAAJ&hl=en.

Peer-Reviewed more than 60 submitted manuscripts for high-impact journals.