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Cutting-Edge SOTA Tools

"Balto, how strongly does my compound bind to my target?"

Best-in-class proprietary and open source tooling for docking, pocket location, and property prediction. Interrogate targets in minutes in a simple conversation. Get Balto to explain outputs or dive into detailed output files.  

The most intuitive interface meets the world's most precise simulation tools.

Simulate protein-ligand interactions

Predict ligand properties (synthesizability, solubility, etc.)
Target active or allosteric sites
Predict ADMET properties of ligands
Quickly and easily evaluate 1000’s of compounds
We're expanding access access to simulation tools rapidly
Easily scale and port compute, analysis, and collaboration

An AI Assistant is only as good as its tools

Benchmarked on the PDBBind 285 set (1)
Benchmarked on DEKOIS 2.0 (2)

ref. 1. https://pubs.acs.org/doi/10.1021/acs.jcim.8b00545, benchmark data available at http://www.pdbbind.org.cn/index.php, used for comparability with prior results
2. https://www.pharmchem.uni-tuebingen.de/dekois/, "DEKOIS 2.0: Demanding Evaluation Kits for Objective In silico Screening"

Cheminformatics benchmarks

logP
Rank
Model
RMSE
1
OCHEM
0.34
2
Deep Origin
0.449
3
DNN (taut)
0.47
4
DNN (mono)
0.5
5
ACD / GALAS
0.5
6
ALOGPS
0.5
7
KOWWIN
0.65
8
JChem
0.72
logD
Rank
Model
MAE
1
Deep Origin
0.425
2
Chemprop - RDKit
0.466
3
Chemprop
0.469
4
BaseBoosting
0.479
5
ContextPred
0.535
6
GCN
0.541
7
AttrMasking
0.547
8
NeuralFP
0.563
9
AttentiveFP
0.572
logS
Rank
Model
MAE
1
Deep Origin
0.525
2
Chemprop-RDKit
0.762
3
AttentiveFP
0.776
4
Chemprop
0.818
5
RDKit2D + MLP (DeepPurpose)
0.827
6
Basic ML
0.828
7
GCN
0.907
8
NeuralFP
0.947
9
CNN (DeepPurpose)
1.023

ref. 1. TDC.Solubility_AqSolDB 
2. Ulrich N., Goss K. U., Ebert A. Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation // Communications Chemistry. – 2021. – Т. 4. – №. 1. – С. 90.
3. TDC.Lipophilicity_AstraZeneca

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