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Genetic algorithms are strong baselines for molecule generation
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Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline. Genetic algorithms (GAs) generate molecules by randomly modifying known molecules. In this paper we show that GAs are very strong algorithms for such tasks, outperforming many complicated machine learning methods: a result which many researchers may find surprising. We therefore propose insisting during peer review that new algorithms must have some clear advantage over GAs, which we call the GA criterion. Ultimately our work suggests that a lot of research in molecule generation should be re-assessed.
Forward citations
Cited by 2 Pith papers
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Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Introduces NMO benchmark using quantum simulations instead of drug proxies for molecular optimization in nanotechnology, where advanced methods underperform simpler ones and a new baseline reveals unknown structural motifs.
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GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem
GLACIER is a single-stage transformer model treating MS/MS fragmentation as subgraph detection on molecular graphs, reporting 70.0% Top-1 accuracy on MassSpecGym and 8x speedup over prior two-stage methods.
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