1. DrugDiscovery@Home The presentation is prepared by: Andrey Voronkov, PhD – speaker (MIPT, Lomonosov Moscow State University) Vladimir Barinov – Grid Dynamics

2. IDEA OF THE PROJECT Computational chemistry + Computational biology + Open code + People’s masses (volunteer computing) = New level of pharmaceutics and biotechnologies Result – a lot of compounds which fast pass preclinical and clinical trials Possibility to create multitarget drugs. Retrosynthetic analysis, modeling of synthetic availability. Filter for toxicity - filter for huge databases including virtual chemical spaces QSAR, side effects Filter for pharmacokinetics Bioinformatics and OMICS data - selection of biotargets Docking and virtual screening. Enrichment (filtration according to activity) Molecular dynamics, FEP, TI

3. «The problem» of the consumer. What problems of the target group does the project solve? 1)Problems of the consumer (pharmaceutical companies, drugs development: -High prices (above 1 billion of dollars) and long period (10-15 years) are required for the development of new medicines -There is a need in new medicines -Impossibility to study the virtual chemical areas (compounds which may be a drug but are not synthesized yet – these is no less that several hundreds of billions of such compounds) 2) Current solutions of the problem: -Biological experiments -Use of leased cluster -Сloud computing 3) Shortcomings: expensive and long. Lack of the community involvement and crowdsourcing. Target groups: -Large, medium-sized and small pharmaceutical companies -Academical research organization leading preclinical development of drugs

4. Product and technology– BOINC, @HOME The method allowing to solve the problems: experiments are simulating in virtual area. Additional description of the technology: -It is the fact that resouces of modern computers / tablet computers / smartphones are in sleep mode and not used for the essential part of time. -Volunteers give their computer resources (various motivations) and we obtain scalable high- performance network. -Segmentation of tasks into subtasks. Validation.

5. Software: -Software with open code for drugs modelling – from the identification of biological targets and computer models of proteins to the pharmacokinetics. -At the moment: software for doking (modelling of the interaction between chemicals and proteins) and molecular dynamics. -Then additionally, we are planning to install software for the proteomics, metabolomics and sequencing data analysis. Project parts Client Server BOINC is the platform with open code that provides interaction between client and server parts of the project. Description of the product and project technology Product is the platform for distributed computing that uses volunteer computer resources for new drugs development.

6. Shortcomings and criticism These methods went out of date and they are inferior to experimental methods of high-throughput screening, or DNA-tag etc. These methods did not go out of date. Statistics shows that the popularity of these methods grows up. However, today this process is not so fast as at the beginning of their use, or it is not so fast as in some other areas, e.g. in biostatistics. But our project also implies that analysis of huge OMICS data will be used for biotarget identification. Our project should be the most price-competitive comparing to any rivals in the area of preclinical trials. Methods are inefficient. In this case, it is most often referred to docking. Docking is effective not for the selection of prospective compounds, but for the sample enrichment, actually, it is one of the filters. At the output of this filter we will set more effective and resource-intensive methods, such as molecular dynamics, perturbation of free energy and thermodynamic integration. Volunteers could refuse to count a commercial project. Our experience shows that it is not true. We have hired thousands of computers in the first weeks of alpha testing. Cloud computing or clusters could replace volunteer computing. They could not. Volunteer computing will always be cheaper. Users replace notebooks and personal computers with smartphones. Smartphones get more and more powerful, and they keep their battery charge longer and longer. There is a possibility to conduct volunteer computing on smartphones. It is difficult to keep confidentiality while we use volunteer computing. Confidentiality is important for aggregate results only, and these results will be protected on the server. Confidentiality is unimportant for intermediate results, but it likely still be kept due to huge volume of data.

7. Currency of the project’s technologies Moreover, the great part of publications in a number of core journals, such as J Med Chem, ChemMedChem include certain methods of computer modeling of drugs. By now, there is a lot of drugs which were developed using computer modeling methods. Talele T.T., Khedkar S.A., Rigby A.C., Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. // Curr Top Med Chem. 2010;10(1):127-41. QSAR

8. PC – protein with active ligand, NC – protein without ligand, o212 – ligand attitude About 12 hours for GPU, for 1 compound. We are planning to get involved 20-30 thousands of GPU. It provides the same speed as in high-throughput screening. Virtual screening using molecular dynamics

9. Business model How does the project earn and plan earnign? 1.Creating new medicines (patents and royalty). Preclinical trials as fast as possible. 2.Working by contract as Contract Research Organization (CRO). 3.Leasing of the computer resources. 4.Solving problems by request using crowd-sourcing (getting users of the platform involved). Distribution (the way to the final buyer): -Direct B2B sales Life hacking: -Crowd-sourcing for the portion of work in the areas of project development and forwarding. -Search for polypharmacological (multitarget) drugs, a new, more promising paradigm. It is more difficult to use high-throughput screening for multitarget drugs creation.

10. Consumer and market -The final market of the project is the pharmaceutical R&D outsourcing market. The project will issue it with preclinical products (amount of money is 27 billions of dollars in 2014): http://www.contractpharma.com/issues/2014-11- 01/view_features/adoption-of-fte-contracts-in-rd-outsourcing-present- and-future). The perfect desirable scenario would be like that: -Several hundred thousands of dollars in two years -Several billions of dollars in 5-6 years -Steady increase up to occupying a leading market position Market share you are planning to occupy – this is difficult to forecast (perfectly - 100 % :-)). A minimum plan is any possible market share)). How far is the market competitive? -The market is highly competitive with a pent-up demand though.

11. Business rivals. Who else is solving the same or an adjacent problem? Technologically, the closest projects are Docking@home и GPUGRID, which are nonprofit and not engaged in new drugs creation. 1.The Docking@home Project performs docking of low-molecular compounds. It is closed. The docking proper is ща little avail. World Community Grid – mainly, docking. 2.The GPUGRID Project uses GPU processors to model proteins and mechanisms of protein–ligand interactions. It is used for high-throughput molecular dynamics. It has good customer reviews. It has standing orders. 3.Several hundreds of companies and research groups are working in the area of preclinical development of drugs in various countries. What are your advantages? The main distinction and advantage of our project is the complex approach to the development of drugs and presence of a great number of applications that allow to solve problems of computer design of drugs, molecular modeling and docking. We also aim at the analysis of genome sequences, usage of collective intelligence and crowd- sourcing for solving the broad range of the problems. Intrinsically, the project proposes to get great number of users involved into the process of drug creation.

12. METRIC 1 The amount of users and expected resources. METRIC 2 Contract volume of R&D services (this includes obtaining grants and financing for the project from the state and from government contracts). METRIC 3 The amount of patents that are filled up and research papers published Current characteristics of the project: Current users: 1769 Curent computers: 5748 Traction

13. Development policy Key points in the project’s development (schedule plan) - Creating a workflow - Advanced: testing and validation of the methods, workflow in experiments for testing drugs - CRO – rendering a service in drug development and computer resources providing - Development of drugs by ourselves

14. Team Voronkov Andrey, PhD in Chemistry, preclinical development of drugs Zaslavskiy Mikhail, PhD, bioinformatician, mathematician, specialist in machine learning, one of the winners, AstraZeneca Dream Challenge Barinov Vladimir, programmer, DevOps

15. Structure of expenses (investment required) -Salaries (for server administrator and programmer, dev-ops is first of all) -Server -Marketing (it may turn out unnecessary – in case of viral dissemination of information)

16. Thanks for your attention! Distributed computing project www.drugdiscoveryathome.com Andrey Voronkov, PhD, R&D, drug design and development av@drugdiscoveryathome.comav@drugdiscoveryathome.com, Tel: +47 46 22 77 96; Skype:digitalbiopharmcom Mikhail Zaslavskiy, IT, server administration, programming, bioinformatics, machine learning http://www.zaslavskiy.org/aboutme.htm Vladimir Barinov, IT, server administration, programming, vb@digitalbiopharm.comvb@digitalbiopharm.com, Mob: +7-903-380-68-30