Molecular generative models partition their coordinate spaces into piecewise-constant identity regions whose chemical organization depends on identity convention, stochasticity, and metric.
Durant, Burton A
6 Pith papers cite this work, alongside 1,917 external citations. Polarity classification is still indexing.
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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 generation quality.
AIBuildAI-2 introduces a knowledge-enhanced agent with a hierarchical evolving external knowledge base that dynamically loads relevant AI development expertise, achieving first place on MLE-Bench at 70.7% medal rate.
SPADE selects ligands more efficiently than deep learning or Bayesian optimization, needing fewer tests on average to identify high-quality drug candidates for novel proteins.
A machine-learning framework that optimizes protein representations with binding-site weighting, PCA, and similarity-based oversampling improves enzyme kinetic parameter prediction, particularly for low-sequence-identity enzymes.
FGR encodes molecules as functional-group bit vectors, embeds them with an autoencoder, and reports competitive or better benchmark accuracy while enabling chemical attribution.
citing papers explorer
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AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models
AIBuildAI-2 introduces a knowledge-enhanced agent with a hierarchical evolving external knowledge base that dynamically loads relevant AI development expertise, achieving first place on MLE-Bench at 70.7% medal rate.
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SPADE: Faster Drug Discovery by Learning from Sparse Data
SPADE selects ligands more efficiently than deep learning or Bayesian optimization, needing fewer tests on average to identify high-quality drug candidates for novel proteins.