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Proprietary models for better drug discovery

Simulate living systems

Predict ligand properties (synthesizability, solubility, etc.)
Target active or allosteric sites
Structure-free docking for protein structure prediction
Broader ligand property prediction including ADMET properties
Multi-level (and scale) models
Easily scale and port compute, analysis, and collaboration

Docking performance across a spectrum of target classes

Benchmarked on the PDBBind 285 set (1)

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

Cheminformatics performance benchmarks

logP
Rank
Model
RMSE
1
OCHEM
0.34
0.49
2
Biosim Props
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
Biosim Props
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
Biosim Props
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

You've got the target, we'll find you the drug candidate

Utilizing our innovative virtual screening technology, we can accelerate your drug discovery program. Our experts can partner with you through each step of the process.

Small molecule discovery

01
Target Structural Dynamics
No crystal structure? Need to model or distrupt protein-to-protein interaction? We've got you.
02
Hit Identification
Find the best binders, account for multiple physicochemical and ADMET properties.
03
Counterscreen against antitargets
Screen for binding against undesired targets to narrow hits and avert safety issues.
04
Lead Optimization
Optimize lead compounds across multiple parameters.

Biologic and large molecule discovery

01
Target structural dynamic predictions
Understand the fluctuation in your target's structure over time.
02
De novo antibody design
Design antibody variable regions to fit the antigen.
03
Antibody-antigen binding prediction
Predict binding conformations of existing antibodies against an antigen.

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We'll get you set up with our resident ML and cheminformatics experts to explore the best solution for you.

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