Featurization or feature engineering is key to the success of machine learning models for chemical and biological systems. In this talk, we will discuss the topological data analysis (TDA) and its combination with machine learning models. Unlike traditional graph/network or geometric models, TDA characterizes only intrinsic structure information thus significantly reduces the data complexity and dimensionality. In topology-based machine learning models, molecular topological fingerprints are generated by persistent homology, which is one of most important tools in TDA. The topological fingerprints are then transformed into feature vectors and combined with machine learning models, including SVM, random forest, CNN, etc. Recently, topology-based machine learning models have made enormous progress in protein-ligand binding affinity prediction and consistently delivered some of the best results in D3R drug design Grand Challenge 2, 3 and 4. Motivated by their great success, we have proposed a series of Hodge-Laplacian based eigen features and combined them with learning models. Our models can achieve state-of-art results in protein-ligand binding affinity prediction.

Topological data analysis (TDA) based machine learning models for biomolecular data analysis

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.