BioAI Therapeutics

🛠️ Building DockFlow — looking for early input DockFlow is currently under active development, with a clear goal: make molecular docking workflows configurable, transparent, and user-friendly for everyday scientific use. Before opening access, we’re collecting input from future users to ensure the tool solves real problems — not just theoretical ones. 🔍 We’re currently looking for Feature requests and workflow ideas Feedback on pain points in existing docking tools Expectations around configuration, automation, and UX 🎁 What’s coming Contributors who help shape DockFlow early will be considered for future premium access once the platform is ready. If you work in molecular docking, cheminformatics, or computational drug discovery and want to influence a tool built for practitioners — now is the right time to reach out. #DockFlow #DrugDiscovery #Cheminformatics #MolecularDocking #BioAI #ProductDiscovery #EarlyFeedback #Startup

🚀 DockFlow — a configurable docking pipeline for modern drug discovery DockFlow is a job-based molecular docking and ligand preparation pipeline built around one principle: 👉 every run should be configurable, reproducible, and fully auditable. What DockFlow already offers: 🔧 Per-job configuration Each run is independently configurable — from ligand and receptor preparation to protonation, 3D handling, validation rules, and docking policies. 📦 Structured, traceable workflows Clean inputs, validated outputs, and a complete audit trail for every job — no black boxes. 🧪 Production-ready preparation stack CIF → receptor + native ligand extraction, SDF/SMILES processing, Meeko-based PDBQT generation, and systematic validation at every step. 🔜 What’s next Integrated ligand generation and optimization, tightly coupled to docking feedback. 📣 We are currently onboarding early users We’re looking for teams who want hands-on influence over features, workflows, and integrations. If you’re building scalable computational chemistry workflows and value control over automation, let’s talk. #DockFlow #DrugDiscovery #Cheminformatics #MolecularDocking #BioAI #Startup #EarlyAccess

🚀 Building a practical docking engine – from theory to a working MVP Over the past months, we’ve been building a modular molecular docking pipeline as part of BioAI Therapeutics. The goal is simple: 👉 make docking reproducible, inspectable, and usable for real drug-discovery workflows — not just one-off scripts. ✅ What we already have working End-to-end docking & redocking pipeline (CLI + Python) Automated receptor preparation cleaning PDBs (ions, cofactors, waters) protonation at physiological pH consistent PDBQT generation Ligand preparation SMILES / SDF input 3D generation, protonation, validation Native-ligand extraction & redocking RMSD-based validation of docking quality Grid generation native-ligand-based and user-defined grids Clear validation reports what files are used where errors come from (bad grids, empty receptors, wrong ligands) 🧠 What the software is good at Running reliable docking benchmarks Debugging why docking fails (not just that it fails) Serving as a foundation for ensemble docking, pharmacophore guidance, and AI-assisted scoring Being target-agnostic (KRAS, IDH1, enzymes, kinases…) 🛠 Tech stack Python · RDKit · Smina/Vina · modular CLI workflows Designed for extensibility, not black-box magic. 🔜 What’s next Ensemble docking Scoring consensus & ranking Ligand optimization loops AI models on top of clean docking data (not instead of it) We are currently running this on real oncology targets (KRAS-G12D, IDH1-R132H) and shaping it into a product. If you’re interested in practical computational chemistry, reproducible docking, or building tools for real scientists — let’s connect. #DrugDiscovery #MolecularDocking #ComputationalChemistry #Bioinformatics #Python #Startup #BioAITherapeutics

🚀 We are excited to share what’s happening at BioAI Therapeutics! We’re building our own software platform for discovering new ligands and docking user-designed molecules — making structure-based drug design faster, smoother, and more accessible for researchers, startups, and students alike. 🎯 Our pilot MVP is launching soon, and we’re now gathering input from early users to help shape what the tool should be able to do. 👉 What would the ideal ligand-discovery / docking tool do for you? We’d love to hear your ideas, needs, and frustrations with existing tools. If you want to join the early pilot or share feedback, drop a comment or send us a message. 🔥 BioAI Therapeutics — accelerating molecular discovery with AI. #bioai #drugdiscovery #cheminformatics #moleculardocking #liganddesign #computationalchemistry #AIinBiotech #startup #biotechinnovation

At BioAI Therapeutics, we combine computational chemistry, AI, and molecular biology to accelerate the discovery of small-molecule therapeutics for some of the most challenging oncology targets. We are currently advancing two core research tracks: 🔬 1) KRAS-G12D – Switch-II pocket inhibitors We are developing an automated ensemble-docking pipeline (smina/Vinardo) with consensus scoring, pharmacophore-guided filtering, and robust receptor/grid preparation workflows. Our goal: identify novel, patent-ready small molecules capable of engaging the SII pocket of the oncogenic KRAS-G12D mutant — a key driver in several solid tumors. 🧠 2) IDH1 R132H – New concepts for glioma-targeted inhibitors Leveraging molecular modeling, docking, and curated literature (Nature Cancer, JCI, Nat Med, etc.), we are designing ligand concepts tailored for mutant IDH1 and its metabolic consequences in gliomas. This includes automated receptor cleaning, grid generation, and large-scale screening of custom libraries. 💡 What drives us The intersection of AI + cheminformatics + molecular biology Reproducible Python-based pipelines A long-term vision to build a fast, precise platform for rational small-molecule design And ultimately: contributing to therapies that may one day help real patients 🌱 We’re a small, fast-moving project — but continuously growing. Precision, transparency, and scientific rigor are at the core of everything we build. If you’re interested in computational drug design, AI in biotech, or want to follow our journey, feel free to connect or follow BioAI Therapeutics.

🚀 Launching BioAI Therapeutics Today marks the beginning of BioAI Therapeutics — a next-generation biotech company harnessing artificial intelligence and molecular simulation to design novel cancer therapeutics. At BioAI, we’re reimagining how oncology drugs are discovered. By combining generative AI chemistry, structure-based design, and protein–ligand modeling, we create small molecules precisely tailored to inhibit key oncogenic targets such as KRAS, BRAF, and PI3K. Our vision is simple but bold: ➡️ Use intelligence — both human and artificial — to overcome the toughest challenges in oncology. We believe that the fusion of AI-driven design and structural biology can cut years from traditional R&D cycles and unlock therapies for patients who currently have none. 🧬 AI + Molecular Design → Smarter Cancer Therapies We’re now entering our first phase of molecule discovery and validation — stay tuned for updates, collaborations, and early results. 📧 bioai.therapeutics@gmail.com #AIforDrugDiscovery #ComputationalBiology #MolecularDesign #StructureBasedDrugDesign #GenerativeChemistry #DrugDiscoveryAI #MolecularSimulation #Bioinformatics #Cheminformatics #VirtualScreening