MOViDA: Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model

Abstract

Drug discovery is a challenging task, characterized by a protracted period of time between initial development and market release, with a high rate of attrition at each stage. Computational virtual screening, powered by machine learning algorithms, has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between features learned by these algorithms can be challenging to decipher. We have devised a neural network model for the prediction of drug sensitivity, which employs a biologically-informed visible neural network (VNN), enabling a greater level of interpretability. The trained model can be scrutinized to investigate the biological pathways that play a fundamental role in prediction, as well as the chemical properties of drugs that influence sensitivity. The model leverages multi-omics data obtained from diverse tumor tissue sources and molecular descriptors that encode drug properties. We have extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the often imbalanced nature of publicly available drug screening datasets, our model demonstrates superior performance compared to state-of-the-art visible machine learning algorithms.

Reliable interpretability of biology-inspired deep neural networks

“…To date, it is unclear whether or how robustness and bias-susceptibility affect different biology-inspired deep learning models. Indeed, broadly reviewing biology-inspired models [13][14][15]17,18,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] , we found that (out of 25 models) only the BIOS model 25 was trained in replicates and only the DTox model 23 3 compared interpretations to networks trained on shuffled labels to rigorously control robustness and network biases.…”