Adaptive Invariance for Molecule Property Prediction
This work addresses the challenge of accelerating COVID-19 antiviral discovery through improved prediction tools, representing an incremental advance by extending invariant risk minimization for heterogeneous molecular data.
The paper tackles the problem of limited generalization in molecule property prediction for COVID-19 antivirals due to scarce or fragmented training data, introducing a method that adaptively forces invariance to nuisance variation and outperforms state-of-the-art transfer learning methods by a significant margin.
Effective property prediction methods can help accelerate the search for COVID-19 antivirals either through accurate in-silico screens or by effectively guiding on-going at-scale experimental efforts. However, existing prediction tools have limited ability to accommodate scarce or fragmented training data currently available. In this paper, we introduce a novel approach to learn predictors that can generalize or extrapolate beyond the heterogeneous data. Our method builds on and extends recently proposed invariant risk minimization, adaptively forcing the predictor to avoid nuisance variation. We achieve this by continually exercising and manipulating latent representations of molecules to highlight undesirable variation to the predictor. To test the method we use a combination of three data sources: SARS-CoV-2 antiviral screening data, molecular fragments that bind to SARS-CoV-2 main protease and large screening data for SARS-CoV-1. Our predictor outperforms state-of-the-art transfer learning methods by significant margin. We also report the top 20 predictions of our model on Broad drug repurposing hub.