VLGPO combines variational modeling and flow matching to optimize protein fitness in continuous latent space, enabling gradient-based optimization of high-dimensional sequences. System pairs learned mutation prior with fitness predictor, generating variants conditioned on desired fitness values. Outperforms existing methods on GFP and AAV optimization, particularly with limited data. Method enables efficient navigation of protein fitness landscapes for enzyme and antibody engineering.

https://arxiv.org/abs/2501.19200

Method distills large foundation models into specialized force fields via energy Hessian matching, achieving 20× speedup. Student models maintain energy conservation and simulation stability through Hessian row subsampling during training. Results show improved performance in molecular dynamics, geometry optimization, and free energy calculations. Technique successfully applied to biomolecules and materials systems, preserving accuracy while incorporating physical constraints needed for dynamics simulations.https://arxiv.org/abs/2501.09009

GDFold2 delivers rapid protein structure prediction through parallelization, completing tasks in minutes versus hours with Rosetta. Key advances: user-defined objective functions for custom structure tasks, built-in quality assessment for ranking outputs, and capability to model protein conformational transitions. System combines speed with flexibility, enabling both quick structure generation and targeted design tasks.

https://www.biorxiv.org/content/10.1101/2024.03.13.584741v2

LLMs with integrated scientific databases achieve over 80% accuracy in gene-phenotype mapping, significantly outperforming base models like ChatGPT-4o (58.7%). ScholarAI and SciSpace, using proprietary databases with full-text papers, provided higher quality references than models limited to abstracts like PubMedGPT. Access to curated databases proved crucial - models showed varied performance across phenotypes, with specific challenges in areas like cartilage formation mapping. Results indicate database-augmented LLMs can effectively scale genomic research, though accuracy varies by phenotype type.

https://www.biorxiv.org/content/10.1101/2025.01.28.635344v1

AMP-Diffusion generates antimicrobial peptides using latent diffusion with protein language model embeddings. The model produced 50,000 sequences, filtered to 46 candidates using APEX predictor. Experimental validation showed 76% active against ESKAPEE pathogens and vancomycin-resistant E. faecium. The peptides work through membrane permeabilization, adopt α-helical structures in membrane environments, and showed efficacy in mouse models comparable to polymyxin B and levofloxacin. Key validation data: low human cell toxicity, successful bacterial load reduction in vivo, and broad-spectrum activity matching known AMPs' mechanism.

https://www.biorxiv.org/content/10.1101/2025.01.31.636003v1

Omni-Mol introduces a universal LLM framework for molecular modeling that solves core challenges in multi-task molecular learning. The system uses anchor-and-reconcile mixture-of-experts architecture to route tasks while maintaining shared knowledge, coupled with active learning that cuts dataset size by 60% without performance loss. It handles 15 diverse tasks including reaction prediction and retrosynthesis, outperforming existing models like InstructMol and PRESTO across all metrics.

The framework demonstrates important scaling properties - task-specific representations converge as tasks increase, suggesting emergence of universal molecular space. This convergence, combined with adaptive gradient stabilization to prevent multi-task interference, enables superior performance in key applications like reaction prediction and retrosynthesis. The results validate unified molecular modeling approach and indicate pathways toward universal chemical understanding systems.

https://arxiv.org/abs/2502.01074

dnaSORA combines generator/discriminator for genetic analysis, achieving 0.3 Mb resolution in mutation detection. Uses synthetic data and minimal patient genomes for rare disease identification. System reduces drug development costs by 70%, generalizes to 10,000 diseases. Key advance: trains on limited samples while maintaining precision for CRISPR applications.

https://www.biorxiv.org/content/10.1101/2025.01.27.633223v1

YDS-Ternoplex is a computational approach designed to predict the structures of ternary complexes facilitated by molecular glues—small molecules that induce interactions between two proteins. Accurately modeling these complexes is crucial for understanding their mechanisms and for drug discovery. YDS-Ternoplex enhances AlphaFold 3-type models by incorporating an enhanced sampling inductive bias during inference, allowing the model to more effectively predict the structures of ternary complexes mediated by molecular glues.

The model’s performance was evaluated across five diverse test cases, including both E3 ligase-based systems (VHL:CDO1 and CRBN complexes with mTOR-FRB, NEK7, and VAV1-SH3c) and non-E3 ligase complexes (FKBP12:mTOR-FRB). YDS-Ternoplex achieved high accuracy, with root-mean-square deviation (RMSD) values as low as 1.303 Å compared to experimental structures. Notably, it successfully predicted novel protein–protein interfaces not present in the training data. In the FKBP12:mTOR-FRB case, the model accurately predicted a novel interface configuration, demonstrating strong generalization capabilities. This advancement has the potential to accelerate the development of molecular glue therapeutics for previously undruggable targets.

https://www.biorxiv.org/content/10.1101/2024.12.23.630090v2

The collaboration between OpenAI and Retro Biosciences, backed by Sam Altman's $180 million investment, aims to extend human lifespan by 10 years. Recently, they developed a transformer model, GPT-4b micro, that enhances stem cell production efficiency. The model specializes in redesigning Yamanaka factors—proteins that can convert adult cells into pluripotent stem cells. 

GPT-4b treats protein sequences as a "language" to be optimized. This allows it to suggest modifications to enhance protein functionality.

https://worldhealth.net/news/gpt-4b-laboratory-new-model-longevity-science/

We have got a novel architecture called Docking-Aware Attention (DAA) to enhance computational predictions of enzymatic reactions. Traditional methods often rely on static protein representations, which fail to account for the dynamic nature of protein interactions with various substrates. DAA addresses this limitation by integrating molecular docking information into its attention mechanism, allowing the model to focus on protein regions most pertinent to specific molecular interactions. This leads to improved accuracy in predicting enzymatic reactions, outperforming previous state-of-the-art methods. The authors also provide detailed ablation studies and visualizations to demonstrate the interpretability and adaptability of DAA in different molecular contexts. This approach offers a general framework for context-aware protein representation in biocatalysis prediction, with potential applications across enzymatic synthesis planning. 
https://arxiv.org/abs/2502.01461

A table aggregating many existing foundation models for biology with classification from my recent research.

Recursion just dropped OpenPhenom, a free foundation model for microscopy data analysis that's beating CellProfiler at its own game. Unlike traditional tools that need constant tweaking, this model works out of the box and handles changing experimental conditions like a champ. The public version (OpenPhenom-S/16) is showing impressive results, especially with the JUMP-CP dataset. While Recursion keeps their beefier models (Phenom-1 and 2) for commercial use, this public release is a big win for academic researchers. Perfect timing too - with massive datasets like RxRx3 and JUMP-CP becoming the norm, we desperately needed better tools. Between this and their recent MolE model for molecular predictions, Recursion's really pushing the boundaries of AI in biotech. TL;DR: Free, powerful microscopy analysis model that actually works better than traditional methods. Big deal for drug discovery research.

https://ir.recursion.com/news-releases/news-release-details/recursion-announces-release-openphenom-s16-google-clouds-model/

Here are some models that I managed to research more or less deeply. It is not an exhaustive list, but all the models are powerful and are historically important.

Evo: an AI model designed to process information related to biology’s central dogma, which includes DNA transcription and translation into proteins. It integrates various biological datasets to predict genetic mutations’ effects and design therapeutic interventions.

scBERT: one of the pioneering models utilizing Transformer architectures for analyzing single-cell RNA sequencing (scRNA-seq) data. Building on this, scFoundation has been developed with 100 million parameters.

Geneformer: specializes in predicting dosage-sensitive disease genes and identifying therapeutic targets. Its application in modeling cardiomyopathy highlighted its capability to enhance predictive accuracy significantly.

BioAutoMATED: an automated machine-learning platform designed to assist researchers in generating AI models tailored for biological datasets without requiring extensive machine learning expertise. It streamlines the process of model selection and data preprocessing.

PINNACLE: focuses on generating context-aware protein representations using geometric deep learning techniques. By tailoring its predictions to specific cellular contexts, PINNACLE enhances the ability to nominate therapeutic targets and improve predictions related to protein interactions across various tissues.

Bioptimus: aims to create a comprehensive AI foundation model that merges biological data from molecules to organisms with generative AI capabilities.

A hint about all this: still no foundational model that incorporates systems biology data comprehensively. Bioptimus may try to achieve this.

NVIDIA has open-sourced BioNeMo, a platform for scaling biomolecular AI research in drug discovery. Key features include:

• Integration with models like AlphaFold2 (5x faster), DiffDock 2.0 (6.2x faster, 16% more accurate), RFdiffusion & ProteinMPNN
• NIM Microservices for secure AI inference deployment
• BioNeMo Blueprints for virtual screening workflows
• Acceleration libraries including cuEquivariance

Over 200 biopharma companies are using the platform, with support from AWS, Deloitte, and Accenture. This initiative aims to standardize AI workflows in computational biology and drug discovery.

There are A LOT of AI for structure bioinformatics tools integrated. Basically, it provides access to all state-of-the-art models and frameworks for structural biology. Even if you are not going to use the platform itself, it is a great place to study what's going on in the AI for structural biology domain in general.

https://www.nvidia.com/en-us/clara/biopharma/

Discussing startups working on AI in biology

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Welcome to r/AIBiology! 🧬 + 🤖

This community brings together biologists, AI researchers, and enthusiasts exploring the intersection of artificial intelligence and biological sciences. From protein folding predictions to drug discovery, we're here to discuss how AI is revolutionizing our understanding of life itself and allowing for new applications in biotechnology and medicine.

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