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Sep 10 2020
Optibrium™, leading providers of software and services for drug discovery, today announced a peer-reviewed publication, ‘Practical Applications of Deep Learning to Impute Heterogeneous Drug Discovery Data’, in the Journal of Chemical Information and Modeling. This paper is testament to the unique ability of the Alchemite™ deep learning algorithm to extract additional value from experimental data in the context of an evolving drug discovery project. In collaboration with the clinical-stage biopharmaceutical company Constellation Pharmaceuticals and technology partner Intellegens, Optibrium’s Augmented Chemistry™ technologies were demonstrated to more accurately guide compound optimization to target high-quality compounds.
Matt Segall, CEO, Optibrium
The study showed that, whereas conventional quantitative structure-activity relationship methods struggle with the inherently noisy and sparse experimental data in drug discovery, the Augmented Chemistry platform, powered by Intellegens’ Alchemite method, provides robust, high-quality predictions. Its ability to exploit correlations between different measurements and explicitly estimate uncertainties in each prediction enables confident decision-making to prioritize experimental work and expedite drug discovery project cycles.
Optibrium’s Augmented Chemistry family of innovative products and services enhance decision making in drug discovery using Artificial Intelligence technologies. These supplement project scientists’ experience and skills by learning from all available data, updated continuously with the latest …
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