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ToolMol is an evolutionary agentic framework that combines a multi-objective genetic algorithm with an LLM-based operator to design small-molecule drugs, achieving state-of-the-art binding affinity and drug-likeness on multiple protein targets.
This article serves as a beginner's guide to protein lead optimization in drug design, specifically explaining the Cradle-1 pipeline and foundational concepts of protein structure and function.
TD3B is a sequence-based generative framework for designing allosteric binders with specific agonist or antagonist behaviors using transition-directed discrete diffusion. The paper introduces a method to control directional transitions in protein states, addressing limitations of static structure-based design.
Researchers developed new antibiotics from scorpion venom and habanero peppers, demonstrating efficacy against resistant bacteria including tuberculosis strains, with ongoing work towards clinical trials.
This paper introduces EDMolGPT, an autoregressive framework that generates 3D molecular conformations from low-resolution electron density point clouds, improving structure-based drug design by leveraging physically meaningful density signals.
This paper introduces SPADE, a novel algorithm for drug discovery that efficiently identifies high-quality ligands from sparse data using only ~40 tests. It demonstrates superior sample efficiency and speed compared to deep learning and Bayesian optimization methods.
Researchers from Universitat Rovira i Virgili published a paper in Nature Machine Intelligence introducing CoCoGraph, an AI tool that generates chemically valid novel molecules using a constrained discrete diffusion process.
OpenProtein.AI, founded by MIT researchers Tristan Bepler and Tim Lu, has launched a no-code platform to democratize access to advanced AI models for protein design and engineering among biologists.
OpenAI announced a new Life Sciences model series designed for biology, drug discovery, and translational medicine, with research and product leads discussing the development approach on the OpenAI Podcast.
OpenAI introduces GPT-Rosalind, a frontier reasoning model specifically designed to support research in biology, drug discovery, and translational medicine.
OpenAI introduces GPT-Rosalind, a frontier reasoning model designed to accelerate research in biology, drug discovery, and translational medicine by optimizing scientific workflows and tool usage.
Google DeepMind and Yale released C2S-Scale, a 27B parameter foundation model built on Gemma for single-cell analysis that discovered a promising drug combination (silmitasertib and interferon) to enhance immune visibility of "cold" tumors, with predictions validated through experimental confirmation.
Orakl Oncology is leveraging the DINOv2 model to integrate machine learning with experimental insights, aiming to accelerate cancer treatment discovery and drug development.
DeepMind's AI tools (including Gemini) reduce the time to analyze bacterial protein structures from years to 6 minutes, and generate unexpected drug design ideas, accelerating the discovery of new antimicrobial drugs, potentially getting ahead of antibiotic resistance.
Google DeepMind's Co-Scientist is a multi-agent AI system that acts as a virtual team of scientists to search literature, generate hypotheses, and design experiments, compressing months of research into days and already yielding new scientific discoveries.
K-Dense-AI released 'Scientific Agent Skills,' an open-source collection of 135 skills for AI agents to perform complex scientific workflows in fields like genomics and drug discovery. It supports various AI models and integrates with tools like Cursor and Claude Code via the Agent Skills standard.
Google Quantum AI researchers explain quantum mechanics basics, qubit superposition, and how early quantum computers could one day simulate molecules for drug screening.