# Drug Discovery Published articles for Drug Discovery. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign DevFeed: [SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [multimodal](), [Transformer](), [datasets]() Tags: [benchmarks](), [drug-discovery](), [generation](), [model](), [multimodal](), [training]() ### AI overview SimpleDesign is a single-stage, end-to-end multimodal generative model for jointly designing protein sequences and three-dimensional structures. It uses Transformer-based multimodal backbones, trains directly in data space on more than 2 million sequence-structure pairs, and achieves competitive results on co-design and unconditional generation benchmarks. ### Source excerpt Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-dimensional structure. Developing generative models capable of understanding this intrinsically multi-modal relationship is crucial for fields like drug discovery and protein engineering. Existing models often rely on a multi-stage training process where autoencoders that tokenize data into latent representations are trained in a first stage. Secondly, a generative model is trained on the latent representation of the autoencoder(s), i.e... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:30:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI research agents](), [OpenSSH]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [bionemo](), [claude](), [code](), [developer-tools-techniques](), [drug-discovery](), [gpu](), [microservices](), [nim](), [nvidia](), [science](), [simulation-modeling-design](), [workflow]() ### AI overview A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models. ### Source excerpt Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... ## Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit DevFeed: [Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-10T13:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [cpu]() Tags: [agentic-ai-generative-ai](), [ai-agents](), [bionemo](), [cuda](), [developer-tools-techniques](), [drug-discovery](), [featured](), [gpu](), [healthcare-life-sciences](), [hpc-scientific-computing](), [inference](), [multi-gpu](), [nvidia](), [performance](), [simulation-modeling-design]() ### AI overview The article describes NVIDIA BioNeMo Agent Toolkit accelerations for biomolecular co-folding workflows, focusing on faster MSA generation, inference, serving, and multi-GPU scaling for drug-discovery workloads. ### Source excerpt Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein... ## A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry DevFeed: [A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry]() Original publisher: [Read original article]() Published: 2026-06-17T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [data]() Tags: [agentic](), [ai](), [autonomous](), [chemistry](), [data](), [drug-discovery](), [gpt](), [models](), [openai](), [research](), [science](), [workflows]() ### AI overview OpenAI and Molecule.one describe a near-autonomous AI chemist using GPT-5.4 and the Maria laboratory system to improve Chan-Lam coupling. The system generated proposals, designed and ran experiments, analyzed data, and achieved higher measured yields for most tested substrates, with humans providing oversight and validation. ### Source excerpt OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research. ## Introducing LifeSciBench DevFeed: [Introducing LifeSciBench]() Original publisher: [Read original article]() Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Benchmark](), [dataset]() Tags: [agentic-ai](), [ai](), [analysis](), [benchmark](), [benchmarks](), [drug-discovery](), [models](), [research](), [science](), [validation]() ### AI overview LifeSciBench is an expert-authored and expert-reviewed benchmark for evaluating whether AI systems can support realistic life science research. It contains 750 tasks across seven workflows and seven biological domains, using expert rubrics to assess reasoning, evidence handling, experiment design, validation, translation, and scientific communication. ### Source excerpt Introducing LifeSciBench, an expert-authored, expert-reviewed benchmark for evaluating how AI systems handle real-world life science research tasks and decisions. ## Building Reliable Agentic AI Systems DevFeed: [Building Reliable Agentic AI Systems]() Original publisher: [Read original article]() Author: Martin Fowler (martin@martinfowler.com) Published: 2026-06-16T12:11:00Z Content type: article Language: en Sources: [Martin Fowler]() Topics: [Artificial Intelligence](), [Retrieval Augmented Generation (RAG)](), [text2sql](), [AI search](), [Generative AI](), [AI-generated research reports](), [data](), [Orchestration](), [observability](), [Prompt Engineering]() Tags: [agentic-ai](), [ai](), [building](), [case-study](), [data](), [drug-discovery](), [efficiency](), [engineering](), [generative-ai](), [information-retrieval](), [language-models](), [llms](), [orchestration](), [production](), [rag](), [research](), [retrieval](), [safety](), [sql](), [technical]() ### AI overview This case study describes PRINCE, a cloud-hosted platform developed by Bayer AG with Thoughtworks for pharmaceutical research. It combines Agentic Retrieval-Augmented Generation and Text-to-SQL to help researchers query decades of safety study reports, answer complex questions, and draft regulatory documents. The article focuses on context engineering, orchestration, recovery, observability, transparency, explainability, human oversight, governance, and compliance in production-ready agentic AI systems. ### Source excerpt One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of information about studies buried in PDF reports. Sarang Sanjay Kulkarni describes its evolution from keyword-based search to an intelligent research assistant capable of answering complex questions and drafting regulatory documents. more... ## Biodefense in the Intelligence Age DevFeed: [Biodefense in the Intelligence Age]() Original publisher: [Read original article]() Published: 2026-06-04T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Resilience](), [Security](), [OpenAI]() Tags: [ai](), [drug-discovery](), [global-affairs](), [health](), [intelligence-age](), [openai](), [research](), [resilience](), [security]() ### AI overview An action plan for using advanced AI capabilities in biology and biodefense, with safeguards, evidence, and governance to support biological security, pandemic preparedness, earlier threat detection, faster countermeasure development, and more coordinated crisis response. ### Source excerpt An action plan for AI-powered biological resilience ## Introducing new capabilities to GPT-Rosalind DevFeed: [Introducing new capabilities to GPT-Rosalind]() Original publisher: [Read original article]() Published: 2026-06-03T13:15:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Benchmark](), [multimodal](), [Optimization](), [coding](), [data]() Tags: [analysis](), [benchmark](), [benchmarks](), [chemistry](), [coding](), [data](), [drug-discovery](), [gpt](), [life](), [model](), [models](), [multimodal](), [optimization](), [product](), [reasoning](), [research](), [validation]() ### AI overview GPT-Rosalind is a specialized model for life sciences research that combines agentic coding and tool use with capabilities in medicinal chemistry, genomics, biological reasoning, and experimental workflows. The article describes its global availability to eligible organizations, performance evaluations, the LifeSciBench benchmark, and MedChemBench's assessment of realistic medicinal chemistry tasks. ### Source excerpt GPT-Rosalind advances life sciences research with enhanced biological reasoning, medicinal chemistry expertise, genomics analysis, and experimental workflow capabilities. ## Introducing GPT-Rosalind for life sciences research DevFeed: [Introducing GPT-Rosalind for life sciences research]() Original publisher: [Read original article]() Published: 2026-04-16T01:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat]() Tags: [ai](), [api](), [chatgpt](), [chemistry](), [codex](), [drug-discovery](), [gpt](), [model](), [openai](), [reasoning](), [research](), [science](), [tool](), [tools](), [workflows]() ### AI overview OpenAI introduces GPT-Rosalind, a frontier reasoning model for life sciences research, including biology, drug discovery, translational medicine, chemistry, protein engineering, and genomics. It is designed to support evidence synthesis, hypothesis generation, experimental planning, and other scientific workflows. ### Source excerpt OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows. ## AlphaFold: Five years of impact DevFeed: [AlphaFold: Five years of impact]() Original publisher: [Read original article]() Author: Demis Hassabis; John Jumper; Pushmeet Kohli; Anna Koivuniemi; AlphaFold team Published: 2025-11-25T16:00:12Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [Bioinformatics]() Tags: [ai](), [drug-discovery](), [explore](), [predictions](), [research](), [science]() ### AI overview The article examines five years of AlphaFold's impact on biological research, describing faster discovery, more novel protein structures, and increased influence on clinical research and patents. It also presents AlphaFold 3, Isomorphic Labs, and the AlphaFold Server as developments advancing AI-assisted drug discovery and digital biology. ### Source excerpt Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.