NovaBio Lab engineers domain-specialized AI architectures bridging long-context frontier models with biophysical simulations, structural docking algorithms, and automated genomic analysis.
Transforming the Computational Biology Bottleneck
Modern drug discovery and structural research teams are overwhelmed with fragmented tools. Crucial binding parameters and experimental protocols remain locked inside unstructured PDF literature.
We build autonomous LLM orchestration engines capable of reasoning over raw biochemical literature, self-correcting input parameters, and driving external software packages via standardized tool-calling protocols.
Engineered for High-Precision Research
Autonomous configuration of target binding pockets, ligand preparation (SMILES to PDBQT conversion), grid box calculations, and docking scoring analysis.
Deep-context synthesis across scientific publications to extract experimental inhibition values (IC50, Ki), mutation sensitivity profiles, and assay setups.
Multi-sequence alignment reasoning (BLAST/FASTA) and mutation tracking to profile antimicrobial resistance (AMR) risks and structural divergence.
Agent Execution Loop
Select a workflow to see how NovaBio Lab coordinates reasoning and computation.
> Target Identified: Human Beta-Secretase 1 (PDB: 1FKN)
> Invoking RCSB PDB API via MCP Server... Fetched 1FKN.pdb (Resolution: 1.90 Å)
> Detecting binding pocket coordinates around catalytic Asp32 / Asp228 residues...
> Grid Center Calculated: [x: 14.28, y: 32.14, z: 9.87] | Size: [24, 24, 24] Å
> Processing Candidate Ligand SMILES: CC(C)CN1CC[C@@H](C1)NC(=O)...
> Running AutoDock Vina simulation (Exhaustiveness: 16)...
> Output Binding Affinity: -9.4 kcal/mol | RMSD l.b.: 0.000 Å
> Generating PyMOL session script: save_complex_1FKN_docked.pml
Model Context Protocol & Reasoning Engine
Multi-source ingestion of biological artifacts.
Frontier models for complex scientific inference.
Standardized biophysical tool connectors.
Structured outputs for laboratory validation.
Product & Research Roadmap
Benchmarking Claude’s structured extraction on 1,000+ peer-reviewed PMC publications covering kinase inhibitors. Evaluating precision against binding assay databases (ChEMBL, BindingDB).
Open-sourcing an MCP connector for AutoDock Vina, enabling any Claude-powered client to directly execute ligand docking simulations, calculate pocket affinities, and render PyMOL sessions.
Extending the pipeline to antimicrobial resistance (AMR) workflows, identifying structural binding mutations across bacterial isolates and modeling reduced inhibitor efficacy.
Deploying pilot instances to partner bioinformatics laboratories to validate in silico predictions against experimental binding assays.
We are collaborating with research groups, computational biologists, and biopharma teams interested in integrating agentic AI into their research pipelines.