In the development of new drugs in the world of life sciences and pharmaceutical industry, everyone in the field knows that this doesn’t happen overnight. Medicinal Chemistry could be considered as one of the important parts of drug discovery being a starting point in determining the compatibility of drugs. However, just like any other industry, it is inevitable for a problem to arise.
Included in the process of drug discovery is “the identification of new leads; their optimization to clinical candidates, and the provision of sufficient amounts of these substances for further studies and for development.” It is considered that this process is one of the most time-consuming parts of a drug’s phase so with the rapid change of technology, part of the struggles being met in performing the processes in drug discovery is coping up and adapting with these changes – which affects the operations lengthening its flow. In identifying leads, it is very important to know which ones are compatible with the other using the appropriate tools. But of course, someone cannot just pick any lead and assign it to a group of compounds – it has to be elaborate, and analysed. For just one mistake, it could lead to instability and disruption.
With the emergence of AI & Machine Learning, there is more likelihood that the time spent is cut therefore advancing the phase, paving way to sooner discovery of treatments for diseases. A lot of experts has agreed that through this innovative application, “leading biopharmaceutical companies believe a solution is at hand.” While it could be a question for some whether it is worth the investment to resort on AI to speed up the isolation of compounds and better determine the best ones that could fit with another, a lot of pharmaceutical companies has already taken the step to apply Artificial Intelligence in their operations for efficiency – “some of the big pharmaceutical companies has been collaborating with IBM Watson Health to perform their operations with utmost productivity.” For an example, “Pfizer is using IBM Watson, a system that uses machine learning, to power its search for immuno-oncology drugs. Another one is Sanofi which has signed a deal to use UK start-up Exscientia’s artificial-intelligence platform to hunt for metabolic-disease therapies, and Roche subsidiary Genentech is using an AI system from GNS Healthcare in Cambridge, Massachusetts, to help drive the multinational company’s search for cancer treatments. Most sizeable biopharma players have similar collaborations or internal programmes.”
Through these initiatives from the large pharmaceutical companies, sooner or later more and more companies in the industry will also adopt to Artificial Intelligence & Machine Learning. While it will take time before this can be observed, it is safe to say that indeed the birth of Artificial Intelligence is now being more recognized in the life sciences industry.
Gathering over a hundred C-level executive from pharmaceutical giants to biotech companies, Proventa International’s long-time running Medicinal Chemistry Strategy Meeting is happening on the 15th of November at The Sheraton Boston Backbay, and one of the topics for discussion will be on within this subject matter along with Design and Custom Synthesis, Integrated Drug Discovery, Chemical Biology & Cheminformatics, Strategic Partnership & Collaboration, and Hit-to-Lead Optimization.
By Nella Ku
Content Strategist, Proventa International