2026 Conference Season
Agentic Causal Graph Learning for Drug Target Discovery
Oral presentation at the AIDD Workshop — fewer than 2% of AAAI submissions receive this honor. Active causal hypothesis testing for drug target identification across the NAD+ biosynthesis pathway.
Neuro-Symbolic AI for Alzheimer's: Biomarker Prediction & Verifiable Intervention Planning
Oral at the LMReasoning Workshop. Physics-informed biomarker prediction with formal verification guarantees — directly applicable to Alzheimer's drug target validation.
De Novo Drug Design, Bayesian Molecular Discovery, and Nanobiomaterials Assistants
Three additional bio-relevant papers across AIDD, AI4Research, and LMReasoning workshops: generative AI for drug design, active learning for molecular screening, and multi-agent reasoning for nanobiomaterials.
Biomedical AI & Alzheimer's Causal Models
Five papers across 3 workshop tracks at the #2 ranked AI conference globally (Google Scholar h5-index: 304). Alzheimer's drug discovery, biomedical reasoning, and verified molecular generation. Publishing alongside Google DeepMind and OpenAI.
Brain Resilience, Cell Painting & Cancer Treatment Optimization
Six papers at the Applied AI for Science conference including 2 oral presentations. Topics spanning brain resilience modeling, high-content cell painting analysis, and AI-optimized cancer treatment scheduling — all leveraging our ACHT and VAP architectures.
6 papers, 2 oral
Applied AI for Science
2025 Conference Season
Active Causal Hypothesis Testing for Drug Target Discovery
Selected for the AI4D3 Drug Discovery Workshop at NeurIPS — the #1 ranked AI conference globally (Google Scholar h5-index: 337). Workshop organized by Harvard Medical School, Genentech/Roche, and AbbVie's Prescient Design team.
Architectural Immune System: Correcting Synthetic Fallacies in AI-Driven Science
Paper accepted at Stanford's Agents4Science Workshop. Advisory board includes Guido Imbens (Nobel Laureate, Stanford), Barbara Cheifet (Chief Editor, Nature Biotechnology), and Eric Topol (Scripps Research). Our framework for detecting and correcting AI hallucinations in scientific discovery.
Physics-Informed Surrogates for Verified Molecular Simulation
Poster at the European Machine Learning excellence network's ML4Molecules workshop. Physics-informed surrogate models that accelerate molecular dynamics by 100-1,000x while maintaining DFT-level accuracy — directly applicable to protein-ligand binding simulations.
Alzheimer's Drug Discovery & Causal Molecular Reasoning
Three papers at the ICLR-affiliated MLGenX workshop focused on Alzheimer's drug discovery. Generative models for neuroprotective compound design with causal ADMET prediction and verified synthesis planning.
ICLR-affiliated
Alzheimer's focus
Validated at the same venues where Google DeepMind publishes AlphaFold
NeurIPS (#1 GS h5:337)
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ICLR (#2 GS h5:304)
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AAAI (#4 GS h5:220)
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Stanford
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ELLIS
Publishing alongside:
Google DeepMind
OpenAI
Harvard Medical School
Genentech/Roche
Stanford
MIT