| goetzpartners securities Limited Evotec AG (EVT-DE): Early adoption of AI in drug design and beyond 21-Feb-2019 / 07:54 GMT/BST Free to access research and investor meetings in a post-MiFID2 world. This research report is intended for use only by persons who qualify as professional investors or eligible counterparties (institutional investors) in the applicable jurisdiction, and not by any private individuals or other persons who qualify as retail clients. Published to the market and investors on 21st February 2019 @ 7.47am (GMT). Evotec AG (EVT-DE): Early adoption of AI in drug design and beyond Recommendation: OUTPERFORM Target Price: EUR25.00 Current Price: EUR20.61 (CoB on 20th February 2019) KEY TAKEAWAY The availability of large, diverse datasets ("big data") in the past 5 - 10 years, coupled with the exponential growth in computing power, increasingly sophisticated algorithms and major advances in experimental techniques for structural biology have firmly propelled artificial intelligence ("AI") into drug discovery ("DD"). A variety of machine learning ("ML") tools are now available across all stages of DD and development. This has led to the emergence of many AI-driven tech players looking to capitalise on biopharma's appetite to incorporate AI into the R&D process, with many partnerships being forged. Through its collaboration with Exscientia, Evotec has been using advanced ML tools to gain operational efficiencies in drug design, reduce the overall time it takes to take a drug candidate from discovery to first in human studies, and increase the chance of success. The first drug candidate may be ready to enter the clinic by YE2019E, when it can potentially be partnered with a biopharma company. Evotec has also recently entered a collaboration with Immuneering to use AI for ligand identification in the field of hereditary metabolic diseases. We reiterate both our OUTPERFORM recommendation and EUR25 target price. Big data the fuel for machine learning, the current form of AI AI refers to systems able to think, behave and perform functions that require human-like intelligence, e.g. decision-making, problem-solving and learning. While the fundamental ideas on AI date back to the 1950s, progress was initially slow due to the lack of data and limited computing power. The availability of large data sets led to a rebirth of AI in the form of ML, the current form of AI where algorithms allow computers to perform tasks intelligently and carry out complex processes by learning from vast amounts of data and examples without the need to rely on hard-coded rules which set out how to solve a problem step-by-step. The better and more comprehensive the data, the higher the predictive power and accuracy. This is because machines can identify complex patterns and relationships in large amounts of unstructured data derived from a variety of sources. The explosion of "-omics" data - particularly genomics, transcriptomics and proteomics - have been a key driver for ML to enter DD. ML tools have permeated every step of the drug R&D process It takes 10 - 15 years and up to $2.6bn to develop one successful drug - up from c.$800m ten years ago and c.$50m in the 1970s. R&D productivity has been declining for years and the current model of passing on the rising costs to patients, insurers and governments is not sustainable. Success rates remain strikingly low, with only one in ten drugs in Phase I likely to succeed, and approx. 70% of drugs fail late-stage clinical trials due to lack of efficacy or side effects. This has led the traditionally conservative biopharma sector to embrace AI in all aspects of drug R&D, ranging from target identification to molecular design and both site selection and patient stratification for clinical trials. In the DD space, laborious and time-consuming steps such as the analysis of the scientific / medical literature, chemical library screens, as well as drug design and synthesis are being enhanced by in silico processes that can be carried out in less time and at a lower cost whilst yielding potentially better molecules. Leading players in the field aim to cut the drug discovery process to 1 year from the current 4 - 5 years. Evotec is implementing Exscientia's algorithms in drug design In April 2016, Evotec signed an explorative collaboration with UK company Exscientia, a world leader in applying AI to molecular design consisting of an experienced team of seasoned big pharma drug hunters with computational chemistry backgrounds, to enhance the work done by its large team of medicinal chemists in the field of drug design, the process of applying chemical transformations to an initial "hit" molecule to optimise factors such as activity, selectivity, physical properties, disposition, half-life and safety. The partnership has been going from strength to strength and prompted Evotec to make a first equity investment in September 2017. The first molecule, an immuno-oncology drug candidate, is expected to enter the clinic by YE2019E - less than four years after the deal was first signed. Kind regards, Brigitte de Lima | Analyst goetzpartners Healthcare Research Team | Research Team goetzpartners securities Limited The Stanley Building, 7 Pancras Square, London, N1C 4AG, England, UK. T +44 (0) 203 859 7725 | brigitte.delima@goetzpartners.com / healthcareresearch@goetzpartners.com www.goetzpartnerssecurities.com goetzpartners securities LinkedIn page Registered in England No. 04684144. Managing Directors: Dr Stephan Goetz, Martin Brunninger and Ulrich Kinzel. goetzpartners securities Limited - Team Members Equity Research Analysts - Martin Brunninger, Brigitte de Lima, Chris Redhead, Martin Piehlmeier and Kieron Banerjee. Sales / Marketing - Erland Sternby. Corporate Finance - Ulrich Kinzel, Wolf Dornbusch and Youchen Xin. Corporate Access and IR - Tanya Tracey and Bettina Ellinghorst. Compliance - Paul W. Dunne. Click here to see our privacy policy. GPSL has a formal client relationship with Evotec AG. 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Free to access research and investor meetings in a post-MiFID2 world. This research report is intended for use only by persons who qualify as professional investors or eligible counterparties (institutional investors) in the applicable jurisdiction, and not by any private individuals or other persons who qualify as retail clients. Dissemination of a CORPORATE NEWS, transmitted by EQS Group. The issuer is solely responsible for the content of this announcement. |
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