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.
6
Pith papers citing it
1,917
external citations · Crossref
representative citing papers
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
-
How Molecular Generative Models Organize Molecular Identity
Molecular generative models partition their coordinate spaces into piecewise-constant identity regions whose chemical organization depends on identity convention, stochasticity, and metric.
-
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 generation quality.
-
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.
-
Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
FGR encodes molecules as functional-group bit vectors, embeds them with an autoencoder, and reports competitive or better benchmark accuracy while enabling chemical attribution.