PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optimized in an unconstrained chemical space have limited practical value if they cannot be synthesized. Policy Gradient for Forward Synthesis (PGFS) is a synthesis-aware reinforcement learning method for molecular improvement, but its use of reactant embedding prediction makes reactant selection indirect, which, as we show, limits learning effectiveness. We first develop PGFS+, in which reaction templates and second reactants are represented by train
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- PossiblePossibly related (embedding) · 58%Guiding generative models to uncover diverse and novel crystals via reinforcement learning →
- PossiblePossibly related (embedding) · 54%A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry →
- PossiblePossibly related (embedding) · 51%Machine learning for drug metabolism prediction: CYPs, Phase II enzymes, and metabolite ID - Drug Discovery News →
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: james”
- LinkedLinked via arxiv author · 85%Boqiao Zhang →
“PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints”
- LinkedLinked via arxiv author · 85%Godbless James →
“PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints”
- LinkedLinked via arxiv author · 85%Sai Krishna Gottipati →
“PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints”
- LinkedLinked via arxiv author · 85%Andrew Fitzgibbon →
“PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints”