Molecular Property Prediction (MPP) is a critical task in computational drug discovery, aimed at identifying molecules with desirable pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Machine learning models have been widely used in this fast-growing field, with two types of models being commonly employed: traditional non-deep models and deep models.

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties.

In recent years, deep neural networks (DNNs) have witnessed extensive applications, and protecting their intellectual property (IP) is thus crucial. As a noninvasive way for model IP protection, model fingerprinting has become popular.

SSL based on persistent homology (PH), a mathematical tool for modeling topological features of data that persist across multiple scales.

The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel.

Neural Information Processing Systems http://nips.cc/

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely tobind toamolecular target inalargelibraryofcompounds.