Lo-Hi: Practical ML Drug Discovery Benchmark
Simon Steshin
Finding new drugs is getting harder and harder. One of the hopes of drug
discovery is to use machine learning models to predict molecular properties.
That is why models for molecular property prediction are being developed and
tested on benchmarks such as MoleculeNet. However, existing benchmarks are
unrealistic and are too different from applying the models in practice. We have
created a new practical \emph{Lo-Hi} benchmark consisting of two tasks: Lead
Optimization (Lo) and Hit Identification (Hi), corresponding to the real drug
discovery process. For the Hi task, we designed a novel molecular splitting
algorithm that solves the Balanced Vertex Minimum $k$-Cut problem. We tested
state-of-the-art and classic ML models, revealing which works better under
practical settings. We analyzed modern benchmarks and showed that they are
unrealistic and overoptimistic.
  Review: https://openreview.net/forum?id=H2Yb28qGLV
  Lo-Hi benchmark: https://github.com/SteshinSS/lohi_neurips2023
  Lo-Hi splitter library: https://github.com/SteshinSS/lohi_splitter