Workshop on Integrative modelling with HADDOCK in conjunction with Workshop on Novel Computational Methods for Structural Biology

Session

Workshop on Novel Computational Methods for Structural Biology

20 Mar 2023, 16:00

Room 1 (BHSS, Academia Sinica)

Conveners

Workshop on Novel Computational Methods for Structural Biology

• Jung-Hsin LIN (Research Center for Applied Science, Academia Sinica)

Contribution list Timetable

4. Different Facets of Distance Geometry

Prof. Antonio MUCHERINO (Institut de Recherche en Informatique et Systèmes Aléatoires, University of Rennes 1, France)

20/03/2023, 16:00

The Distance Geometry Problem (DGP) asks whether a simple weighted undirected graph G=(V,E,d) can be realized in the K-dimensional Euclidean space so that the distance constraints implied by the weights on the graph edges are satisfied. This problem was proven to be NP-hard in the context of graph embeddability, and has several applications. In this talk, we will focus on various currently...

5. A Curvilinear-Path Umbrella Sampling Approach to Characterizing Thermodynamics and Mechanisms of Biomolecular Interactions

Dr Dhananjay JOSHI (Research Center for Applied Science, Academia Sinica)

20/03/2023, 16:30

Protein-protein and protein-ligand interactions are central in biological mechanisms. These interactions can be classified into thermodynamics and mechanistic pathways. Estimating accurate and reliable interaction energetics along the thermodynamic pathway is one of the ongoing challenges in computational biophysics. Umbrella sampling simulation-based potential of mean force calculations is...

6. Quantum Machine Learning for Structure-Based Virtual Screening of the Entire Medicinal Chemical Space

Prof. It has been estimated based on the graph theory that there are at least 1060 organic molecules that are relevant for small-molecule drug discovery. Using machine learning to estimate the binding free energies for screening of large chemical libraries to search for the tightly binding inhibitors would take a considerable amount of computational resources, yet it is not possible to explore the entire biologically relevant chemical space. Quantum computing provides a unique opportunity to accomplish such a computational task in the near future. Here, we demonstrate how to use 512 occupancies to describe the structures of protein-ligand complexes, how to convert the classical occupancies to the quantum states using nine qubits, and to estimate the binding free energies (Gbind) of the complexes using quantum machine learning. We showed that it is possible to use only 450 parameters to prepare the quantum states for describing the structure of one protein-ligand complex. In this work the entire 2020 PDBbind dataset was adopted as the training set, and we used 45 parameters as the first attempt to construct the model for predicting the binding free energies (Gbind). The Pearson correlation coefficient (PCC) between the estimated binding free energies and the corresponding experimental values are 0.49. By slightly increasing to 1,440 parameters for constructing the neural network model for the prediction of the Gbind, the PCC is improved to be 0.78, which is even slightly better than to the results achieved by recent classical convolutional neural network models using more than millions of parameters. In this work, for the first time, we demonstrated the feasibility of using quantum computers to explore the entire medicinal chemical space with a concrete, implementable approach. LIN (Research Center for Applied Science, Academia Sinica)

20/03/2023, 17:00

It has been estimated based on the graph theory that there are at least 1060 organic molecules that are relevant for small-molecule drug discovery. Using machine learning to estimate the binding free energies for screening of large chemical libraries to search for the tightly binding inhibitors would take a considerable amount of computational resources, yet it is not possible to explore the...