Assay Central
One of the best ways to find new drugs is to leverage knowledge of previous attempts, successful and otherwise.
We have developed a user friendly web-based software to access 16 machine learning algorithms: Deep learning 3 layers (classification), Adaboosted decision trees (classification & regression), Bernoulli Naïve Bayes (classification), Laplacian Bayes (classification) & BayesianRidge (regression), K-nearest neighbors (classification & regression), Random forest (classification & regression), Support vector classification (classification & regression), LogisticRegression (classification) & ElasticNet (regression), XGBoost (classification & regression). We use ECFP & FCFP descriptors with diameter 4/6/8 along with bit folding and leave out validation options. We provide several data visualization options. Assay Central machine learning models can be used to filter and score compounds prior to testing. The following images illustrate how the classification and regression model statistics can be displayed for BACE1 datasets.
Benefits
Making data accessible to machine learning
Data intensive visualization resulting from these many models
Closing the loop between experimentalist and data repositories
Graphical display of models – instant feedback
Model applicability – multiple methods to assess with scores and graphics.
Access
We can use Assay Central in fee for service work for you.
We can provide an annual license for access to this software.
We provide maintenance and customization options.
Success stories
We have worked on collaborative projects with companies on a fee-for service basis :
Worked with a major US consumer product company to collate public estrogen receptor data and score their chemicals.
With a major pharmaceutical company to model their ADME data and propose compounds to synthesize.
With a preclinical CRO to model their internal drug screening data make predictions for future testing as well as evaluate blood brain barrier permeation.
Multiple collaborations on whole cell and target specific models – have identified novel inhibitors for academic collaborators.
Model building and testing with academics with access to previously unpublished data
Built and validated transporter models for different probes and shared models.
We can work with you to automate the curation of your in house data and build machine learning models that you need to generate novel insights.
If you are a VC or company we can use our models to perform independent due diligence to evaluate in-licensing opportunities or score company pipelines in your portfolio.
If you are a company we can develop next generation leads using our models, library enumeration and retrosynthesis tools.
We have developed extensive capabilities using recurrent neural networks to design therapeutics de novo.
We have expertise with using our models with molecules large and small such as macrolactones and proteolysis targeting chimeras (PROTACs).
We have capabilities to custom develop approaches to model molecule properties as well as spectra.
Please contact us to hear about our case studies.
RECENT PAPERS ON THE TECHNOLOGIES INVOLVED INCLUDE:
Repurposing Approved Drugs as Inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins Syndrome
Exploiting machine learning for end-to-end drug discovery and development
Opportunities and challenges using artificial intelligence in ADME/Tox
Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets
Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads
High Throughput and Computational Repurposing for Neglected Diseases
Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Comparing and Validating machine Learning Models for Mycobacterium tuberculosis Drug discovery
Open Source Bayesian Models. 2. Mining a "big dataset" to create and validate models with ChEMBL