Adaptive Invariance for Molecule Property Prediction

Understand

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.