LAYER / 01 RESOLVEDOrganism
Disease phenotype
Resolve the anatomical and functional signatures that distinguish disease from health.
AI-NATIVE BIOLOGY WORKSPACE
Model life.Decode disease.
MindCell brings virtual cell modeling, disease phenotype intelligence, and specialized biomedical AI into one agent-orchestrated research environment.
- + Agent-routed
- + Model-aware
- + Traceable
LIVE IN-SILICO RESPONSE
06organelles modeled
SCROLL TO DISCOVER
AI-NATIVE DRUG DISCOVERY
From AI disease modeling
to drug discovery.
Build a computable disease model across organism, cell, and molecule—then use the same biological model to screen interventions and identify therapeutic targets.
01Build the disease modelPHENOTYPE · CELL · MECHANISM
LAYER / 01 RESOLVED
LAYER / 02 RESOLVEDCell
Cellular phenotype
Trace tissue-level change to the cell states, morphology, and functions that drive it.
LAYER / 03 RESOLVEDMolecule
Molecular mechanism
Connect cellular dysfunction to actionable pathways, proteins, and molecular events.
02Turn the model into discoveryMODEL · SCREEN · PRIORITIZE
STAGE 01MODEL
AI disease modeling
Build a computable disease model from multimodal phenotype and patient evidence.
+ MULTIMODAL+ CAUSAL
STAGE 02SCREEN
Cellular drug screening
Run in-silico perturbations and prioritize compounds by phenotype rescue, not proxy alone.
+ PERTURB+ RANK
STAGE 03TARGET
Target identification
Surface intervention points linked to mechanism, predicted response, and supporting evidence.
+ MECHANISM+ EVIDENCE
OUTPUTMechanism-linked therapeutic hypotheses
Traceable from phenotype to target.
FOUNDATION MODELS, ONE WORKSPACE
The model hub for living systems.
Run proven biological AI models through one research interface. MindCell routes your question to the right specialist, keeps inputs and outputs together, and makes every result traceable.
MindCellModel HubROUTED BY AI
AlphaFold 3
Complex structure
Boltz
Structure & affinity
OmegaFold
Single-sequence folding
OpenFold
Open structure platform
DiffDock
Molecular docking
RFdiffusion
Generative design
ProteinMPNN
Sequence design
ProGen2
Protein generation
ESM
Protein language model
Evo 2
Genomic language model
RiNALMo
RNA language model
Uni-Mol
Molecular foundation model
MindCell research blog
Ideas, methods, and evidence.
01
How to Map Protein Aggregation Propensity with Aggrescan3D and a PDB Structure
Static Aggrescan3D 1.0.2 analysis of the official RCSB Protein Data Bank structure 2GB1 produced finite scores for all 56 residues of chain A. The minimum score was −3.5629, the maximum was 1.1983, and the arithmetic mean was −1.4135875. The highest-scoring residue was valine A:21 at 1.1983; methion
Read article →02How to Align PDB Protein Structures with PyMOL and Interpret RMSD Correctly
PyMOL 3.1.0 aligned the official RCSB/PDBe 1D3Z ubiquitin NMR structure as the mobile object to the official RCSB 1UBQ X-ray ubiquitin structure as the target. The exact cmd.align return was [0.39735108613967896, 449, 5, 1.2869186401367188, 602, 381.0, 76]: refined RMSD 0.397351 Å across 449 atom pa
Read article →03How to Reconstruct Ancestral DNA Sequences with ancseq and IQ-TREE
The validated workflow selected the IQ-TREE model from the supplied alignment, rooted the analysis with AVR-Mgk5GE162, reconstructed 13 ancestral records, and produced all six required final deliverables. The sorted state table contained 68,807 bytes of explicit state probabilities; intermediate IQ-
Read article →04How to Annotate and Number an Antibody Light Chain with ANARCI and Kabat
ANARCI recognized a human kappa light chain spanning residues 0–106. The retained HMM score was 197.0 with an e-value of 1.6 × 10⁻61, and the assigned germlines were IGKV1-1201 and IGKJ101. Both the Kabat-numbered CSV and the HMM evidence table passed semantic validation. This result passed native e
Read article →05How to Estimate Aqueous Solubility from SMILES with the ESOL Model
The model predicted logS values of −0.0424460527 for ethanol, −1.9325820263 for benzene, and −2.3143366025 for octanol. The expected ordering ethanol benzene octanol was recovered, and the output retained molecular weight, LogP, rotatable bonds, aromatic proportion, SMILES, and units. This result pa
Read article →RESEARCH COLLABORATION
COSINX
Build models with us.
For research partnerships, scientific collaboration, and MindCell enquiries, contact our team directly.
research@cosinx.com