Machine & Deep Learning

Research &Development in scientific domains generate enormous amount of raw and unorganized data. In order to organize the data and use it to get some key insights, the processes of Machine & Deep Learning can be utilised. The techniques can be used to structure big complex data that we can comprehend and utilize for our benefits. The data can either be clustered or classified using various algorithms of unsupervised and supervised learning algorithms respectively. Unsupervised learning infer

patterns from data and supervised learning can classify data. Artificial neural networks and Support vector machines are popular algorithms of supervised learning.

At Novel Techsciences we presently employ such techniques in the fields of Protein Engineering, Drug discovery and in the development of predictive algorithms based on biological data. We strongly believe that Machine & Deep Learning can deliver faster and reliable organized data that can used to save precious time and money which can be directed towards further efforts in scientific research.

In the area of Protein Engineering, sequence to function models are built to characterize stabilizing mutations, mutations that can enhance enzyme specificity, affinity and shelf life. This is achieved without requiring prior understanding of the biological pathways. The only pre-requisites are examples of protein sequences and their functions for the program to learn.

The stages where machine learning is applied in Drug discovery are designing a drug’s chemical structure, investigating the effect of a drug, prediction of drug likeness properties like ADME and Toxicity which can greatly speed up the overall process of drug development and reducing the resources needed to bring a much-needed pharmaceutical solution to the market.