Early Phase Drug Screening with Machine Learning

Significantly accelerate drug screening by reducing costs and research time during drug discovery.

Benefits this Solution Provides

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Profit From Our Substantial Expertise

Take advantage of our vast experience applying ML in drug discovery and development.

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Speed Up Drug Screening and De Novo Design

Apply ML-based quantitative structure-activity relationship (QSAR) and inverse QSAR models.

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Accelerate Developability Assessment

Evaluate developability score with ML-based prediction models.

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Reduce Costs

Improve the efficiency of in-depth analysis of individual data for favorite candidates.

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ML in Small-Molecule Screening and Design

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  • Screen libraries of chemical compounds in silico (virtual screening) and classify them based on their binding affinity to the desired target through QSAR modeling; avoid wasting resources conducting in vitro screening for all candidates.
  • Use generative deep learning models to design de novo drugs. By exploring the continuous space of properties, it is possible to generate molecules with novel scaffolds and desirable properties (inverse QSAR).

In Silico Developability Prediction Using ML

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  • For the most promising candidates identified by virtual screening or de novo synthesis, in addition to activity, access its physicochemical properties to fit the desired product profile.
  • Use ML algorithms such as Neural Networks, Partial Least Squares regression (PLS), Support Vector Machine (SVM) regression (SVM), and Random Forest regression (RF) to screen for properties and predict developability.

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ML in Process Development

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  • After selecting the final candidate, apply ML to process development to design a process suitable for producing a high-quality product, as emphasized by the Quality by Design initiative.
  • Envisage ML to support process optimization in silico as so-called digital twins to minimize the experimental effort for process development and validation.

What Can You Expect From Implementing Our Solution?

Before After
Large chemical structures are tedious and expensive to investigate using conventional approaches. ML models can explore, in silico, larger chemical structures space while saving resources.
High failure rates. Low failure rates with a priori in silico screening.
A few million compounds can be screened in the laboratory. In silico predictions can screen billions of compounds.
Drug discovery remains an expensive and time-consuming process. Drug discovery becomes a more cost- effective, faster process.

Learn More From Our Experts

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Blog Post

Emerging Technology

Harnessing the Power of Machine Learning for R&D in Drug Discovery

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Industry Insight

Risk and Data as Knowledge Enablers: A Lifecycle Approach

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Webinar

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