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<\/a>Machine learning is emerging as a potential solution to approach this process with more efficiency and lower cost. The idea of computer-aided drug discovery is not new. But the recent explosion of data available, rapid development of graphics processing units (\u201cGPU\u201d) and cloud-based computing has made deep learning and big data modeling possible [3]. Machine learning can be extremely helpful in the following aspects: more efficient screening of new compounds, optimizing molecular designs against multiple property profiles of interest, and identifying synthetic routes to realize the composition.<\/p>\nPfizer: Dipping Toes in The Water<\/strong><\/p>\n In December 2016, Pfizer announced a collaboration with IBM that will use IBM Watson for Drug Discovery platform in the pharmaceutical giant\u2019s immune oncology R&D.<\/p>\n The IBM Watson for Drug Discovery is a cloud-based platform that utilize deep learning, natural language processing and other cognitive reasoning to support researchers in finding hidden connections in science. The platform has been fed with more than 25 million abstracts, more than 1 million full-text journal articles and 4 million patents in order to streamline the drug discovery process. By contrast the average researcher reads between 200 and 300 articles in a given year [4].<\/p>\n Pfizer expects to use the platform to analyze massive volumes of disparate data sources, including publicly available clinical and scientific data as well as Pfizer\u2019s proprietary lab data, to discover immune-oncology therapy candidates and the possible combination of them [5].<\/p>\n In addition to the collaboration with IBM, in 2018, Pfizer launched new collaboration with the Chinese tech startup XtalPi for small-molecule modeling. According to their joint announcement, Pfizer and XtalPi are currently collaborating in crystal structure prediction and screening\u2014using computer models to determine the potential molecular stability of an organic compound\u2014and are looking to advance their work in drug design and solid-form selection [6].<\/p>\n All in AI?<\/strong><\/p>\n Pfizer\u2019s approach to AI is a cautioned experimentation, rather than a full-on embrace. Pfizer is not alone among big pharmas in adopting this strategy. Roche (Genetech) announced collaboration with GNS Healthcare, Sanofi collaborates with Exscientia, GlaxoSmithKline signed a similar deal with Exscientia and Insilico Medicine. Their strategy begs the question of whether they have done enough. If machine learning has the potential to be the disruptive technology in pharmaceutical industry\u2019s drug discovery process, market leaders like Pfizer can only solve the \u201cinnovator\u2019s dilemma\u201d through founding or acquiring an independent subsidiary and equipping it with the necessary resources [7].<\/p>\n Machine learning has the unique feature of positive reinforcement\u2014the more data one has, the better algorithm can be developed, the better one\u2019s algorithm is at predicting results, the more customers it attracts with even more data available. Big pharma\u2019s collective inertia to develop their own machine learning platform leads to the concentration of data in a handful of tech startups to whom the big pharmas outsource the work. Pfizer, and other market leaders, might be breeding a potential competitor in the high potential space of growth.<\/p>\n In the medium term, Pfizer needs to establish its own machine learning analysis team in house or make acquisition of startups in the space. Indeed, machine learning\u2019s application in drug discovery has not been proven clinically, therefore, a strategy of \u201cAll in AI\u201d involves considerate amount of risk. But if Pfizer wait until full proof of machine learning\u2019s superiority, what is a cue today might well turn into a tiger to bite off Pfizer\u2019s market dominance.<\/p>\n Questions Remain<\/strong><\/p>\n Despite all the hype and hope machine learning has offered, drug discovery has never been and certainly will not be an easy field. At the heart of any machine learning application is the quest for high-quality data to train the algorithm. However, many of the existing scientific research data sets likely have high noise levels and may have inconsistent standards, impacting the comparability across different experiments or laboratories. New challenges to deal with heterogeneous data sets and experiment conditions remain unsolved. Will they block the application of machine learning in drug discovery? How should market leader like Pfizer position itself against this particular challenge?<\/p>\n <\/p>\n <\/p>\n [1] Frost & Sullivan, \u201cInfluence of Artificial Intelligence on Drug Discovery and Development,\u201d July 18, 2018.<\/p>\n [2] Pharmaceutical Research and Manufacturers of America, \u201c2016 Biopharmaceutical Research Industry Profile,\u201d http:\/\/phrma-docs.phrma.org\/sites\/default\/files\/pdf\/biopharmaceutical-industry-profile.pdf<\/a>.<\/p>\n [3] Zhang, Lu et al, \u201cFrom Machine Learning to Deep Learning: Progress in Machine Intelligence for Rational Drug Discovery,\u201d Drug Discovery Today, Volume 22, Issue 11, November 2017, Pages 1680-1685, https:\/\/doi.org\/10.1016\/j.drudis.2017.08.010<\/a>.<\/p>\n [4] Van Noorden R, \u201cScientists May Be Reaching a Peak in Reading Habits\u201d, February 3, 2014, http:\/\/ibm.biz\/BdrAjS<\/a>.<\/p>\n [5] Pfizer, \u201cIBM and Pfizer to Accelerate Immuno-Oncology Research with Watson for Drug Discovery,\u201d December 1, 2016, https:\/\/www.pfizer.com\/news\/press-release\/press-release-detail\/ibm_and_pfizer_to_accelerate_immuno_oncology_research_with_watson_for_drug_discovery<\/a>.<\/p>\n<\/a>(765 words)<\/p>\n