{"id":73,"date":"2020-10-14T13:38:19","date_gmt":"2020-10-14T17:38:19","guid":{"rendered":"http:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/?p=73"},"modified":"2020-10-14T13:38:19","modified_gmt":"2020-10-14T17:38:19","slug":"chemists-and-analytics-a-surprising-but-fruitful-partnership","status":"publish","type":"post","link":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/2020\/10\/chemists-and-analytics-a-surprising-but-fruitful-partnership\/","title":{"rendered":"Chemists and Analytics, A Surprising but Fruitful Partnership"},"content":{"rendered":"\n

My first experience as a CADS intern was standard to many. I\nworked with two other students and a faculty advisor on a project for a\ncorporate client. The project followed a typical and expected process from\nintroduction of the problem, lots and lots of industry research, applying\nanalytical solutions to said problem, and then a final recommendation and\npresentation to the client. Once finished, I was excited and looking forward to\na similar experience the following semester. However, before the semester\nended, my team\u2019s faculty advisor alluded that my skills may be put to the test\nnext semester on a project with the chemistry department. Little did I know\nthat this opportunity would teach me more about chemistry, analytics, data\nscience, and the intersection of them, than I could have ever imagined. <\/p>\n\n\n\n

For a little background, I am a current senior at Miami\nUniversity where I am studying finance and business analytics. I was introduced\nto CADS and knew this was something I wanted to be involved with. It was an\nopportunity to use the skills and knowledge I had gained in the classroom,\nalong with developing new ones, to fun and interesting projects. When one of my\nprofessors, Dr. Weese, mentioned she wanted me to be involved in a project for\nMiami University\u2019s chemistry department, I was immediately intrigued. Never did\nI imagine I could apply my skills to a problem faced by my university\u2019s\nchemists. That is, until our analytics team met with the chemistry team when we\nall realized the amount of untapped potential this partnership held. This\npartnership consisted of undergraduate students, graduate students, PhD\ncandidates, professors, and even a department head from the Chemistry and\nInformation Systems & Analytics departments at Miami University. <\/p>\n\n\n\n

When you think about it, much of the typical chemist\u2019s work\nis repetitive and manual. Compounds are researched, tested, and experimented\nwith all by hand for the most part. Computers and robots can automate some of\nthis if you have enough resources, but the point is that most every part of\nthis process normally has to be done by hand, either a human\u2019s or a robot\u2019s.\nThe advent of machine learning and artificial intelligence has already\ntransformed many industries by eliminating, or at the minimum reducing, much of\nthese tedious tasks. Thanks to Dr. Zishuo \u201cToby\u201d Cheng and his curious mind,\nthe question \u201cWhy can\u2019t we apply machine learning to our beta lactamase\ninhibitor research?\u201d was posed. What I loved most about this proposition is\nthat nobody had tried anything exactly like it before. There was every reason\nfor this partnership to work; we had the data, the smarts, and the desire, just\nnothing to go off of. However, this wasn\u2019t an issue or disadvantage at all;\ninstead, it forced us to think outside the box and think of every possible way\nto do something and see what worked and, many times, what didn\u2019t. Not that\nhaving something to model after is ever bad, but it\u2019s just human tendency to\nlatch on to what was done before as the correct way. In our work, just about\neverything we did was \u201cright\u201d, only because there wasn\u2019t anything to prove\notherwise. <\/p>\n\n\n\n

After several months being on the job, I think it is safe to\nsay this partnership has been a huge success. By throwing numerous data science\nand analytical methods at the problem, we were able to dwindle down the search\nspace of unknown compounds from over 70,000 to just 3,000. When you consider\nhow in a normal situation every one of these 70,000 compounds would have to be\ntested, it becomes quickly clear how important this was feat was. No longer do\nyou have to take a complete shot in the dark and hope you find a good compound;\ninstead, you are able to look through only the compounds that have the highest\nprobability of being successful per our models. Pending the results of the high\nthroughput screening of these 3,000 compounds, we could eventually apply our\nanalyses and models to a database of millions of unknown compounds. <\/p>\n\n\n\n

It was in these times where the partnership really shined.\nAs analytics students with no background in chemistry more advanced than high\nschool chemistry, all of our results meant little to nothing to us. However, with\nour knowledge of what the numbers were showing and the chemists\u2019 knowledge of\nwhat the numbers represented, we were able to uncover some incredible insights.\nFor example, we strategically employed models with some form of\ninterpretability that gave insight into what features of a compound make a good\nbeta lactamase inhibitor. A couple of the most important variables made sense\nand were already well known as important features to the chemists. However,\nthere were several features of good inhibitors according to our models that had\nnever been considered before. The chemists determined these features still made\nlogical sense, but simply were things not seen in past research. Although it\nisn\u2019t the discovery of the next greatest beta lactamase inhibitor yet, it is\ninsights like these that validate we are on the right track and give a glimpse\nin to the incredible potential for interdisciplinary teams like ours. <\/p>\n\n\n\n

What\u2019s next? For our team, we will continue to explore better methods for supporting the chemists\u2019 research of beta lactamase inhibitors, hopefully leading to further insights into these important compounds. On a much larger scale, I hope to see many more partnerships like this one arise around Miami University. I can imagine successful partnerships with areas all over Miami. Thanks to CADS, these partnerships aren\u2019t a matter of if they will ever happen, it\u2019s simply a matter of when. <\/p>\n\n\n\n

About the Author<\/h3>\n\n\n\n
\"\"
Mitch Fairweather<\/a> is a Miami University senior studying Finance and Business Analytics <\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

My first experience as a CADS intern was standard to many. I worked with two other students and a faculty advisor on a project for a corporate client. The project followed a typical and expected process from introduction of the problem, lots and lots of industry research, applying analytical solutions to said problem, and then […]<\/p>\n","protected":false},"author":3098,"featured_media":74,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"_s2mail":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-73","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-student-projects"],"_links":{"self":[{"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/posts\/73","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/users\/3098"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/comments?post=73"}],"version-history":[{"count":0,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/posts\/73\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/media\/74"}],"wp:attachment":[{"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/media?parent=73"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/categories?post=73"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.miamioh.edu\/the-center-for-analytics-and-data-science\/wp-json\/wp\/v2\/tags?post=73"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}