Incredible medical discoveries are revolutionizing our ability to treat and even cure patients; but, drug discovery and development is becoming more difficult and costly, leaving many patients without viable medicines.
Simultaneously, throughout the last decade, a revolution in machine learning has enabled answers to issues that were formerly deemed intractable. Machine learning approaches can currently caption photos, translate across languages, and identify voices at or above human performance levels.
One of the systems utilized in AI is neural networks, which may be used to identify chemical structures with medicinal significance. A neural network uses a large set of training data containing information about the chemical structure-biological activity relationship, which is preceded by successful neural network training and acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity.
The data is derived through experimental observations as well as from relevant quantum models. There were constraints in biological data a few years ago – while access to huge, rich data sets has spurred machine learning’s development, such data sets are still rare in biology, where data collection remains essentially artisanal. Recent advances in cell biology and bioengineering are now allowing us to change this by facilitating the generation of huge volumes of biological data. Besides, researchers have revealed that neural networks have a substantial capacity to create generalizations based on even very restricted training data.
Pipelines for drug discovery and development are lengthy, complicated, and dependent on a variety of factors. Machine learning (ML) techniques offer a collection of tools that can enhance discovery and decision-making for well-specified queries with a large amount of high-quality data. Opportunities to use ML arise at various phases of drug development.
Instead of depending on restricted “discovered” data, we have solutions that use contemporary biology technologies to build high-quality, huge data sets designed for machine learning, allowing us to unleash the full power of modern computational methodologies.
Our solutions are created by professional biologists and drug hunters who collaborate with cutting-edge technologists and machine learners. A group of life scientists and data scientists, software engineers, process engineers, bioengineers, translational scientists, and drug hunters are collaborating to answer problems that we would never have thought to ask on our own.
For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com
