Target context boosts performance in scarce-data regimes when fused properly via FiLM but degrades results under distribution shift, while standard molecular benchmarks suffer from severe leakage and trivial baselines.
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7 Pith papers cite this work, alongside 1,203 external citations. Polarity classification is still indexing.
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Starling, a multi-agent LLM system, extracts ~6.3 million nuanced structured records from PubMed across six tasks with reported error rates of 0.6-7.7%, lower than several curated databases.
On chemistry SMILES with a fixed 165-token base, BPE and Unigram-LM produce near-disjoint vocabularies (Jaccard ≤0.161) and Unigram-LM emits 29–41% more tokens across 22 matched conditions.
A semi-autonomous AI agent team using a 246M-parameter graph diffusion model generated 5,231 novel molecules across two oncology targets, with 91.9% new Murcko scaffolds and strong predicted binding performance.
AIMBio-Mat is a conceptual blueprint for an AI-native, FAIR, governance-aware decision layer that formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty.
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
ML models predict inhibitors for BACE-1, AChE and GSK-3β with reported AUC-ROC scores above 0.9 and accuracies of 85-87%.
citing papers explorer
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When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
Target context boosts performance in scarce-data regimes when fused properly via FiLM but degrades results under distribution shift, while standard molecular benchmarks suffer from severe leakage and trivial baselines.
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Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale
Starling, a multi-agent LLM system, extracts ~6.3 million nuanced structured records from PubMed across six tasks with reported error rates of 0.6-7.7%, lower than several curated databases.
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Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES
On chemistry SMILES with a fixed 165-token base, BPE and Unigram-LM produce near-disjoint vocabularies (Jaccard ≤0.161) and Unigram-LM emits 29–41% more tokens across 22 matched conditions.
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Rhizome OS-1: Rhizome's Semi-Autonomous Operating System for Small Molecule Drug Discovery
A semi-autonomous AI agent team using a 246M-parameter graph diffusion model generated 5,231 novel molecules across two oncology targets, with 91.9% new Murcko scaffolds and strong predicted binding performance.
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AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation
AIMBio-Mat is a conceptual blueprint for an AI-native, FAIR, governance-aware decision layer that formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty.
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On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Pre-training GNNs on ECFP prediction produces statistically significant QSAR gains on five of six Biogen benchmarks with OOD splits, but underperforms on heterogeneous datasets and complex endpoints like binding affinity.
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Machine learning-based modeling to predict inhibitors for targets of Alzheimer's Disease
ML models predict inhibitors for BACE-1, AChE and GSK-3β with reported AUC-ROC scores above 0.9 and accuracies of 85-87%.