Friends and colleagues often ask, “What are the top 100 important "AI in Biology" papers that provide broad insights into the field?” 📚🔬 While narrowing it down to exactly 100 is no small feat, I’ve curated a list of foundational and impactful #BioAI papers. I'm sure the list would exceed more than 100 given the relentless expansion of this thrilling field! Please follow this thread for key insights, and please feel free to suggest any papers I may have missed! With my background and interests, I prioritize papers in #AgingBiology, #CellBiology, and #Neuroscience. I’ll keep this thread pinned to my profile and update it regularly. I encourage everyone to add more papers from their own expertise—let’s make this interactive and foster engaging discussions! Here is a collection of essential #AIbio papers. #100_AIBio_Papers #AIBio_papers #AIBio_Chat #AIneuro_papers #AI_AgingBio_papers

Please retweet!! Thank you Are you a journal editor, editorial board member, or involved in the editorial process and looking to commission a timely review or perspective on #BioAI or #NeuroAI? I’d be happy to explore opportunities to write a review covering emerging intersections of AI, neuroscience, biology, and biomedical research. If you are interested in inviting a review, perspective, or commentary, feel free to reach out or DM me. I’d be glad to discuss potential topics and scope. Here is a sample article that received lot of attention here on @X nitter.cf/compose/articles/edit/… Please retweet!! Thank you #BioAI #NeuroAI #Neuroscience #Neurotechnology #BrainAI #ScientificPublishing #AcademicPublishing

Ability of a Structural World Model to Detect Cryptic Pockets from Apo Structure biorxiv.org/content/10.64898… Summary: This study presents a method for identifying cryptic drug-binding pockets directly from a single apo protein structure, without requiring molecular dynamics simulations, conformational sampling, co-folding predictions, or external pocket-detection tools. The approach uses a proprietary structural world model that generates a per-residue latent representation from protein coordinates. This latent state is read out as a cryptic-lining score, suggesting the model implicitly captures conformational flexibility that other methods must explicitly simulate. Predicted pockets are constructed as residue sets rather than fixed geometric spheres. High-scoring residues seed candidate pockets, which are expanded into distinct, non-overlapping predictions through a residue-growth strategy and geometric non-maximum suppression. On CryptoBench (231 test proteins), the model achieves 84.8% top-1 and 95.2% top-5 localization accuracy. Similar performance is observed on a CryptoBank subset, with 84.6% top-1 and 99.0% top-5 accuracy. Residue-level classification is strong (AUC = 0.8465), although exact pocket-boundary recovery remains more challenging under stricter overlap criteria. The method generalizes well to unseen proteins, recovering the known WRN helicase allosteric site at rank 1 across multiple apo structures after all WRN proteins were removed from training. A key advantage is its ability to place the true cryptic site at rank 1, addressing a common limitation of ensemble-based approaches. Compared with single-structure baselines such as P2Rank, DeepPocket, PocketMiner, and fpocket, the model achieves substantially higher top-1 hit rates. It also complements the ensemble-based method OpenDDE, identifying many cryptic sites missed by OpenDDE while retaining all of OpenDDE’s top-5 hits. Overall, the work demonstrates that latent representations learned by a structural world model can effectively detect and rank cryptic pockets from apo structures alone, with performance improving as training data increases. #DrugDiscovery #CrypticPockets #BioAI #AIforScience #ProteinStructure

Molecular heterogeneity of Aging across populations.

Single-Cell Mapping of Transcriptomic Vulnerability in Brain Disorders

Curious about synthetic data in BioAI and NeuroAI? Simulators, virtual cells, model collapse, and one model that went from R² 0.999 to 0.15 on real neurons. Here it is 👇 nitter.cf/BioAI_NeuralNet/status… #BioAI #NeuroAI #Foundationmodels

A multimodal, all-optical platform for linking cell identity to metabolic function in intact tissues nature.com/articles/s41592-0…

ResolVI: addressing noise and bias in spatial transcriptomics nature.com/articles/s41592-0…

A foundation model of vision, audition, and language for in-silico neuroscience arxiv.org/abs/2605.04326 #Foundation_models, #NeuroAI, #NeuralNets

Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism mdpi.com/1099-4300/22/5/516

Oligodendrocytes Across the Spectrum of Central Nervous System Health and Disease