- Predictive validity in drug discovery: what it is, why it matters and how to improve it
- Notes on end-to-end biology
- Machine Learning for Protein Engineering
- Inside the nascent industry of AI-designed drugs
- Tapping into the drug discovery potential of AI
- The digital and analog worlds of protein engineering
- Artificial intelligence for natural product drug discovery
- AI-powered therapeutic target discovery
- AI & Multi-Omics Data in Drug Development
- Has AI changed the course of Drug Development? My own blog post!
- 'The more overhype, the harder you fall': Schrödinger CEO warns on AI craze
- AI Poised To Revolutionize Drug Development
- Where is generative design in drug discovery today?
- Diffusion Models in Generative Chemistry for Drug Design
- MISATO - Machine learning dataset for structure-based drug discovery. nearly 20,000 experimental structures of protein-ligand complexes + associated properties:
- PoseCheck is a package for analysing the quality of generated protein-ligand complexes from 3D target-condtioned generative models
- ProteinFlow is an open-source Python library that streamlines the pre-processing of protein structure data for deep learning applications. ProteinFlow enables users to efficiently filter, cluster, and generate new datasets from resources like the Protein Data Bank (PDB) and SAbDab (The Structural Antibody Database).
- Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
- Drug target prediction through deep learning functional representation of gene signatures
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ML protein engineering seminars https://www.ml4proteinengineering.com/
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ProtMamba: a homology-aware but alignment-free protein state space model https://github.com/Bitbol-Lab/ProtMamba-ssm
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Robust deep learning–based protein sequence design using ProteinMPNN | Science https://www.science.org/doi/10.1126/science.add2187
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Illuminating protein space with a programmable generative model
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Generative Diffusion Models for Antibody Design, Docking, and Optimization
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ProteinFlow is an open-source Python library that streamlines the pre-processing of protein structure data for deep learning applications. ProteinFlow enables users to efficiently filter, cluster, and generate new datasets from resources like the Protein Data Bank (PDB) and SAbDab (The Structural Antibody Database https://github.com/adaptyvbio/ProteinFlow
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Efficient evolution of human antibodies from general protein language models https://twitter.com/ideasbyjin/status/1656640057384501248?s=51&t=sLukUyq0ReWrcwOwgUR_XA
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De novo design of protein interactions with learned surface fingerprints
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Large language models generate functional protein sequences across diverse families
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A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction https://github.com/csi-greifflab/Absolut. Unconstrained lattice antibody-antigen bindings generator - One tool to simulate them all!
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Generative language modeling for antibody design https://twitter.com/jeffruffolo/status/1606058881171406848
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Excited to share our new computational method, APPRAISE, that ranks engineered proteins by how likely they bind to a target. Fast, accurate ranking of engineered proteins by receptor binding propensity using structural modeling https://twitter.com/dingxiaozhe/status/1618257727515676672?s=46&t=f8b5WHByAZFWrIkVXOgyAQ
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Unlocking de novo antibody design with generative artificial intelligence
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Ten quick tips for sequence-based prediction of protein properties using machine learning
- High-accuracy mapping of human and viral direct physical protein-protein interactions using the novel computational system AlphaFold-pairs
- Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy
- AlphaFold touted as next big thing for drug discovery — but is it?
- https://mobile.twitter.com/SurgeBiswas/status/1613232556673224705
- AI-driven drug discovery companies, BenevolentAI, recently underwent layoffs and restructuring in response to a PhIIa failure of their atopic dermatitis program
- DeepMind AlphaFold for antibody discovery: What's the status? Experimental conclusion: We performed a very easy experiment to check if there’s “a free lunch” for antibody discovery employing AlphaFold2. Unfortunately, according to our results, this is not the case"
- Has AI discovered drug? https://www.science.org/content/blog-post/has-ai-discovered-drug-now-guess
- Why AlphaFold won’t revolutionise drug discovery