Exploration and Comparison of Modern AI Algorithms to Predict Drug Efficacy

We compare various Machine Learning and Deep Learning algorithm variants and string embeddings to predict the pIC50 values against the JAK2 protein target. The paper works upon improving the existing predictor component in the ReLeaSE framework by comparing various models such as Random Forest Regressor and Convolutional Neural Network for prediction. The paper also works on using different SMILE string embeddings such as PPMI matrices and OpenBabel fingerprints for molecules to improve upon the current baseline accuracy. Using a data set consisting of an approximate of 2000 data points, the models were trained on 80:20 split. The comparison of various models provides us with insights on the robustness in using SMILES as a representation for molecules. The results show that the use of Random Forest Regressor along with OpenBabel fingerprinting to encode SMILES performed the best with an R2 score of 0.71, which improves the baseline predictor in ReLeaSE by a ΔR2 of 23%. Our models when trained on an augmented version of the JAK2 dataset gave an R2 score of 0.97