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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.
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Cited by 3 Pith papers
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NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization
NMR-Solver recovers the correct molecular structure in 52.9% of curated experimental JACS cases (top-1, formula plus 1H/13C), outperforming a transformer baseline at 14.4%.
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NovoMolGen: Rethinking Molecular Language Model Pretraining
A 1.5-billion-molecule pretrained transformer family, NovoMolGen, sets new state-of-the-art results in de novo and goal-directed molecule generation, and shows pretraining loss correlates only weakly with downstream g...
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Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
LLMol fine-tunes an LLM on simplified SELFIES and uses GRPO with RDKit-derived rewards for targeted molecular generation, but its own benchmark tables contradict the claimed state-of-the-art performance.
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