✦New: High-throughput DFT pipeline
The AI
Platform
For Chemists
Find optimal conditions faster, select candidate structures with predictive models, and understand the chemistry driving your results.
Reduce experiments by
90%
Faster condition discovery
10x
100x
Faster DFT
<5 min
to first result
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Partners at leading institutions
Partners at leading institutions
Features
Discover what our platform can do for you
Process Optimization
Machine learning guided process optimization
Accelerate your entire process optimization pipeline.
Decrease number of experiments by 50-90% vs DOE and traditional methods.
LLM literature agent helps design parameter space.
Extract insights driving your chemistry using feature importance analysis and contour plots.
Everything you need to accelerate discovery and optimization
From molecular design to experiment optimization — a unified platform for machine learning in chemistry.
Process Development
Iteratively Optimize Experiments
Log experimental results, run AI-guided optimization rounds, and receive experimental recommendations.
Explainability
SHAP Analysis
Understand which features drive your model predictions with interactive SHAP value beeswarm plots.
Optimization
Multi-Dimensional Optimization
AI models navigate high-dimensional parameter spaces, learning from each experiment to converge on optimal conditions faster.
Prediction
Molecular Property Prediction
Predict molecular properties with custom ML models trained on your data for maximum relevance to your chemical space.
Core Tool
Atom Label Editor
Label atoms and bonds to calculate site-specific descriptors like bond angles, dihedrals, and atom-level electronic properties. Auto-assign consistent labels across your library with our common-substructure algorithm.
c1ccc(-c2nc(C3COC(c4ccccn4)=N3)oc2)cc1
Labels
A
Nsp2 ring N
B
Nsp2 ring N
C
Oring O
D
Oring O
E
Cring C
F
Cring C
G
Cring C
H
Cring C
8 labeled · MCS
Computational Chemistry
DFT, Automated and GPU Accelerated
A fully automated, GPU-accelerated pipeline handles conformer searching, geometry optimization, electronic structure calculations, and descriptor extraction to yield high-quality descriptors ready for model training.
AI-Powered
Reaction Recommender
Input starting materials and target products, then get AI-recommended catalysts, ligands, solvents, and conditions backed by literature.
Who Chemetrian is for
Built for every stage of R&D
From academic research to industrial manufacturing — one platform that adapts to how you work.
CROs & CDMOs
- Accelerate molecule delivery timelines
- AI-driven synthesis route optimization
- Win more contracts with faster results
Reaction Optimization
Optimized yields
Suzuki coupling — 96.4% yield (exp #17)
C–H arylation — 94.8% yield (exp #22)
Buchwald–Hartwig — 91.2% yield (exp #11)
Process Chemists
- Find optimal conditions faster than DoE
- AI literature analysis for starting conditions
- Predict untested catalyst and reagent activity
Surrogate Model
Temperature85°C
Catalyst loading2.5 mol%
Solvent ratio3:1 THF/H₂O
Predicted yield: 94.3% ± 1.2%
Medicinal Chemists
- Predict activity and ADMET across candidate libraries
- Identify trends driving your target with interpretable models
- Filter libraries with dimensionality reduction and clustering
Predicted pIC50 — BACE-2 Inhibitors
SMILES
pIC50
O1CC[C@@H](NC(=O)[C@@H](Cc2cc3cc(ccc3nc2N)-c2ccccc2C)C)CC1(C)C
9.2
S(=O)(=O)(CCCCC)C[C@@H](NC(=O)c1cccnc1)C(=O)N[C@H]([C@H](O)C[NH2+]Cc1cc(ccc1)CC)Cc1cc(F)cc(F)c1
8.7
S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C
8.5
FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCCC2=O)cc(NCC)c1)Cc1ccccc1
7.4
Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@@]1(N=C(N)C(=O)N(C1)C)C
4.5
Academic Researchers
- Automated DFT workflows for high-fidelity descriptors
- Build predictive models from computed features
- Optimize and discover new chemistry
Descriptor Calculation Lab
1
Conformer search
2
Geometry optimization
3
DFT calculation
4
ML-ready descriptors
200+ descriptors per molecule
Materials Scientists
- Predict HOMO–LUMO gaps, Tg, tensile strength, charge-transport properties, and more
- Design novel materials from structural polymers to OLED emitters
- Optimize the synthesis of materials with target properties
HOMO–LUMO Gap Prediction
TCTA — predicted levels
LUMO–2.43
3.40 eV gap
HOMO–5.83
Material
HOMO
LUMO
GAP
TCTA
-5.83
-2.43
3.40
CBP
-6.00
-2.90
3.10
NPB
-5.40
-2.30
3.10
mCP
-5.90
-2.40
3.50
Teachers
- Already launched in undergraduate chemistry curricula
- Hands-on AI/ML and chemistry experience for students
- Ready-made workflows for coursework and labs
Lab Handout
CHEM 241 · Organic Chemistry Lab
Lab 3 — Predicting Drug Potency with Machine Learning
Building a QSAR model for BACE inhibitors using molecular fingerprints
Duration: 2 hours · Submit via Chemetrian platform
1 Background
Beta-secretase 1 (BACE-1) is a key therapeutic target in Alzheimer's disease. Quantitative structure-activity relationship (QSAR) modeling enables rapid virtual screening of candidate inhibitors. In this exercise you will use Morgan fingerprints to encode molecular structure and train a regression model to predict inhibitory potency (pIC50).
2 Learning Objectives
•Generate molecular fingerprints from SMILES strings
•Train and evaluate a Random Forest regression model
•Interpret R² and RMSE as measures of model quality
CROs & CDMOs
- Accelerate molecule delivery timelines
- AI-driven synthesis route optimization
- Win more contracts with faster results
Reaction Optimization
Optimized yields
Suzuki coupling — 96.4% yield (exp #17)
C–H arylation — 94.8% yield (exp #22)
Buchwald–Hartwig — 91.2% yield (exp #11)
Process Chemists
- Find optimal conditions faster than DoE
- AI literature analysis for starting conditions
- Predict untested catalyst and reagent activity
Surrogate Model
Temperature85°C
Catalyst loading2.5 mol%
Solvent ratio3:1 THF/H₂O
Predicted yield: 94.3% ± 1.2%
Medicinal Chemists
- Predict activity and ADMET across candidate libraries
- Identify trends driving your target with interpretable models
- Filter libraries with dimensionality reduction and clustering
Predicted pIC50 — BACE-2 Inhibitors
SMILES
pIC50
O1CC[C@@H](NC(=O)[C@@H](Cc2cc3cc(ccc3nc2N)-c2ccccc2C)C)CC1(C)C
9.2
S(=O)(=O)(CCCCC)C[C@@H](NC(=O)c1cccnc1)C(=O)N[C@H]([C@H](O)C[NH2+]Cc1cc(ccc1)CC)Cc1cc(F)cc(F)c1
8.7
S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C
8.5
FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCCC2=O)cc(NCC)c1)Cc1ccccc1
7.4
Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@@]1(N=C(N)C(=O)N(C1)C)C
4.5
Academic Researchers
- Automated DFT workflows for high-fidelity descriptors
- Build predictive models from computed features
- Optimize and discover new chemistry
Descriptor Calculation Lab
1
Conformer search
2
Geometry optimization
3
DFT calculation
4
ML-ready descriptors
200+ descriptors per molecule
Materials Scientists
- Predict HOMO–LUMO gaps, Tg, tensile strength, charge-transport properties, and more
- Design novel materials from structural polymers to OLED emitters
- Optimize the synthesis of materials with target properties
HOMO–LUMO Gap Prediction
TCTA — predicted levels
LUMO–2.43
3.40 eV gap
HOMO–5.83
Material
HOMO
LUMO
GAP
TCTA
-5.83
-2.43
3.40
CBP
-6.00
-2.90
3.10
NPB
-5.40
-2.30
3.10
mCP
-5.90
-2.40
3.50
Teachers
- Already launched in undergraduate chemistry curricula
- Hands-on AI/ML and chemistry experience for students
- Ready-made workflows for coursework and labs
Lab Handout
CHEM 241 · Organic Chemistry Lab
Lab 3 — Predicting Drug Potency with Machine Learning
Building a QSAR model for BACE inhibitors using molecular fingerprints
Duration: 2 hours · Submit via Chemetrian platform
1 Background
Beta-secretase 1 (BACE-1) is a key therapeutic target in Alzheimer's disease. Quantitative structure-activity relationship (QSAR) modeling enables rapid virtual screening of candidate inhibitors. In this exercise you will use Morgan fingerprints to encode molecular structure and train a regression model to predict inhibitory potency (pIC50).
2 Learning Objectives
•Generate molecular fingerprints from SMILES strings
•Train and evaluate a Random Forest regression model
•Interpret R² and RMSE as measures of model quality