Machine learning is not a new entry to the pharmacological field. Scholarly articles explaining the use of machine learning techniques for pharmaceutical data analysis may be found dating back decades. The traditional paradigm has been to use a few techniques on a particular dataset and publish a paper comparing results to prove the efficacy of a particular technique . BenevolentAI turns this paradigm on its head by using the vast and growing volume of pharmacological literature as the basis for datasets on which to use a variety of machine learning techniques to discover new drugs .
To produce a new drug, tens of thousands of candidate drugs are needed. The Pharmaceutical Research and Manufacturers of America (PhRMA) has created a brief video which explains the process:
Most pharmacological research does not result in breakthrough discoveries. PhRMA illustrates the rarity of a new drug discovered from the magnitude of potential drugs here :
Not shown in the figure above is a depiction of how much research is required preceding drug discovery, but that is where BenevolentAI operates today . Instead of discarding research which does not immediately lend itself to the further development of a particular drug, BenevolentAI harnesses both clinical trials data and academic papers to produce new potential medicines . The final products are potential new drugs. Machine learning is not just important for BenevolentAI, it is essential.
Natural prerequisites for the development of such technology are corporate knowledge of the pharmacological literature as well as the talent required for developing new machine learning techniques which can digest this knowledge. Talent for developing machine learning algorithms is scarce and in high demand, as evidenced by the compensation machine learning scientists may receive . In 2016 BenevolentAI recruited Dr. Jerome Pesenti, the former chief scientist of IBM Watson . In the current environment of fierce competition for data scientists, retaining talent like Dr. Pesenti will be challenging.
Since BenevolentAI is still considered a startup, its future direction is not completely clear, though some reporting does give us a hint of where the firm is headed. In an interview with Business Insider, BenevolentAI’s founder, Ken Mulvany “predicted BenevolentAI would be selling its own drugs ‘in the next four years’” . To develop its own capabilities as a platform for developing drugs and for analyzing previous research, BenevolentAI will inevitably have to expand its operations to perform these new functions. Both the pharmaceutical industry and the emerging machine learning landscape are complex. Maintaining harmony between the two aspects of this business will require sustained effort.
The company already has made progress towards this goal. It has produced “24 drug candidates in just four years” and has sold drug targets to a US company . Navigating the regulatory environment of pharmacological development in both the US and UK is increasingly challenging . One recommendation may be to use the machine learning talent embedded in BenevolentAI’s workforce to ensure compliance with increasing regulatory burdens.
As BenevolentAI works towards new drug solutions and continues to refine its own processes, it faces competition from larger industries and similar startups. In the same year Jerome Pesenti left IBM, his previous employer teamed up with Pfizer to work towards drug discovery . Exscientia, another UK based startup, also uses machine learning for drug discovery and has partnered with Sanofi . In response to these challenges, BenevolentAI should capitalize on its strength of harnessing the power of untapped information available in the literature.
As we have learned that the power of machine learning grows with the quality and quantity of information on which it is trained, BenevolentAI should consider expanding the base of literature on which it trains beyond articles published in English. An emerging technique known as neural machine translation can be used to translate written language . For example, if BenevolentAI were to add Chinese research into its corpus, it could discover new insights at the intersection of research previously disconnected by the language barrier.
Key questions moving forward may include the following. How can a firm continue to recruit top talent when many of the professors that would teach these techniques have been drawn away from academia by extraordinary salaries at AI-based firms? Additionally, since drug development can take years, how can a firm retain top talent as it expands to perform new functions? Aside from neural machine translation, what other techniques might BenevolentAI consider incorporating into its machine learning arsenal?
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