FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
Molecular De Novo Design through Deep Reinforcement Learning
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abstract
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.
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UNVERDICTED 2representative citing papers
Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.
citing papers explorer
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FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
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Quantum-inspired Reinforcement Learning for Synthesizable Drug Design
Reinforcement learning with a quantum-inspired simulated annealing policy neural network is applied to synthesizable molecular optimization and reports competitive results against genetic algorithm baselines on the PMO benchmark with a 10K query budget.