Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani

For effective representation of molecular structure of amino acids, the individual atoms of amino acids of antibody and antigen were treated as undirected graph, where the atoms are nodes and bonds are edges 88 . It has been shown that graph representation is better in transferring the chemistry and topology of molecular structure compared to Extended Connectivity Fingerprints (ECFP) 88, 89 . We construct these molecular graphs using RDkit 90 . Embeddings are generated to encode relevant features about the molecular graph 91, 92 . These embeddings encode information like the type of atom, valency of an atom, hybridization state, aromaticity etc. First, each antibody and antigen were encoded into separate embeddings and then concatenated into a single embedding for the entire antibody-antigen complex. We then apply mean pooling over the features for this concatenated embedding to ensure dimensional consistency across the training data. The pooled information is then passed to classifier algorithms like XGBoost 93 , Random Forest 94 , Multilayer perceptron, Support Vector Machine (SVM) 95 and Logistic Regression which then predict whether the antibody is capable of neutralizing the virus.