Cheminformatics workflows, augmented with machine learning (ML), play a crucial role in drug discovery projects at NUVISAN, where we leverage them to analyze complex biological and chemical data, accelerate the pipeline, and provide innovative solutions. ML algorithms help us identify patterns in large datasets that would be difficult, if not impossible, for humans to detect, and apply the learning for the selection and design of novel molecules with desired properties. When partnering with us, we can:
- develop and apply predictive ML models for various physchem and ADMET end points based on client or public datasets, or even from our Life Science Database. By using the ML models for property prediction, we characterize and select the best hits from hit finding experiments and support DMTA cycles or molecular optimization to help reduce cycle times.
- leverage quantitative SAR models for data analysis, identification of activity cliffs, and compound optimization by suggesting chemists what compounds to make next, based on the available SAR.
- deploy de novo generative chemistry models to design new molecules with improved properties. Our multiparameter optimization (MPO) strategy suggests novel chemical equity while fulfilling your desired profile.
If you are looking for a partner to help you accelerate your drug discovery program, we invite you to contact us today to learn more about our cheminformatics and data capabilities and how we can help you achieve your goals.
