CECAM

Progress and developments of artificial intelligence for drug design

Location : CECAM-IT-SIMUL, Italian Institute of Technology, Genoa, Italy
June 17, 2019 – June 19, 2019

APPLICATION CLOSED !!!

If you write to us, at this point we are not sure to have extra space. We will do our best to accept more applicants, given the high interest in this event. However, please be aware that we may NOT respond to your application, at this point.

 

IMPORTANT INFORMATION FOR APPLICANTS:

  1. THERE IS NO FEE TO ATTEND TO THE WORKSHOP
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  3. SEND US A TITLE AND ABSTRACT, IF YOU PLAN TO PRESENT A POSTER 

WE MAY SELECT A FEW POSTERS FOR SHORT ORAL PRESENTATIONS.

PARTICIPANTS WILL BE SELECTED FROM APPLICANTS, AND ACCEPTANCE WILL BE COMMUNICATED BY END OF MARCH. 

YOU CAN CHECK IF YOUR NAME ALREADY APPEARS IN THE LIST OF PARTICIPANTS – WHICH MEANS YOU ARE PART OF THE WORKSHOP.

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After decades of incremental innovations in drug design, artificial intelligence (AI) tools have started to show promise revolutionizing the way finding new drug targets, identifying viable chemical starting points, designing efficient synthesis routes and defining novel therapeutic markers for translational studies.

The new concept and the associated technologies have the potential alleviating the numbers game in drug discovery as well as making pharma R&D more efficient by automation In early discovery AI can support finding better drugs against well characterized targets by the analysis and prediction of druggable binding sites and exploitable interactions, as well as for a target deconvolution/identification. Deep learning algorithms can transform screening programs by being taught to design optimized compounds and increase hit rates utilizing the knowledge encoded in large published and proprietary datasets. Further applications involve repositioning and redesign programs that search in the known set of drugs to find pharmacotherapies for new indications.

Medicinal chemistry teams realizes multiple benefits from these tools. In drug design, AI technologies are perfectly suited for solving multidimensional optimization problems that is typically the case in drug discovery settings. In synthetic chemistry, deep learning can suggest viable alternatives how to best synthesize the designed compounds. Optimization of chemical reactions using contributes to provide the best compound at the best time that might deliver lower attrition rates as compounds move through the pharma value chain. The efficiency of these processes are often enhanced by automated technologies in both synthesis and testing that provides a previously unseen amount of data feeding and improving AI-based design technologies. Finally, AI technologies have significant impact on clinical research. Its advancement to the clinical trials results the process faster, cutting costs, improving trial quality, and reducing trial times. Finding disease biomarkers and gene signatures support recruiting eligible clinical trial patients and opens new perspectives in personalized therapies. The present workshop covers the key areas of AI driven drug discovery including the theory and practice in target identification, hit finding, drug design and synthetic chemistry.



References

1. Ching et al, Opportunities and Obstacles for deep learning in biology and medicine, J. R. Soc. Interface 15:20170387
2. Segler et al, Planning chemical syntheses with deep neural networks and symbolic AI, Nature, 555 p604, 2018
3. Schwaller et al, “Found in Translation”: Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Model, arXiv:1711.04810
4. Merk et al, De Novo Design of Bioactive Small Molecules by Artificial Intelligence, Mol. Inf., 37, 1700153, 2018
5. Pant et al, Design, Synthesis, and Testing of Potent, Selective Hepsin Inhibitors via Application of an Automated Closed-Loop Optimization Platform, J. Med. Chem., 61, p4335, 2018
6. Gómez-Bombarelli et al, Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules, ACS Cent. Sci., 4, p268, 2018
7. Segler et al, Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks, arXiv:1701.01329
8. Altae-Tran et al, Low Data Drug Discovery with One-Shot Learning, ACS Cent. Sci., 3, p283, 2017

Australia

Mark P. Waller (invited speaker) (Pending AI)

Belgium

Vineet Pande (Janssen Pharmaceuticals)
Dries Van Rompaey (Janssen)

Canada

Alán Aspuru-Guzik (invited speaker) (University of Toronto)

China

Shuguang Yuan (Shenzhen Institute of Advanced Technology, Chinese Academy of Science)

France

Oscar Mendez Lucio (Bayer SAS)
Quentin Perron (invited speaker) (Iktos)

Germany

Matteo Aldeghi (Max Planck Institute for Biophysical Chemistry)
Vikram Reddy Ardham (TU Darmstadt)
Bartosz A. Grzybowski (invited speaker) (Center for Soft and Living Matter, Institution for Basic Science, South Korea)

Great Britain

Alexander Hammer (University of Glasgow)

Hungary

Greg Makara (invited speaker) (ChemPass)

Ireland

Hansel Gomez (Nuritas)

Italy

Mariangela Agamennone (Università degli studi di Chieti)
Giorgio Amendola (Università degli studi della Campania)
Maicol Bissaro (University of Padova)
Giovanni Bolcato (UNIPD)
Diego Dal Ben (University of Camerino)
Sergio Decherchi (invited speaker) (Italian Instiute of Technology, Genoa)
Diego Liberati (Politecnico di Milano)
Stefano Piotto (Università degli studi di Salerno)
Davide Sabbadin (Autifony)
Lucia Sessa (Università degli studi di Salerno)

The Netherlands

Marta Arimont Segura (Vrije Universiteit Amsterdam)

Poland

Adam Hogendorf (Polish Academy of Sciences)

Saudi Arabia

Mohammed Alanazi (DEPT OF PHARMACEUTICAL CHEMISTRY, COLLEGE OF PHARMACY, KING SAUD UNIVERSITY.)

Spain

Alberto Cuzzolin (Acellera)
Jose Jimenez-Luna (Universitat Pompeu Fabra)
Tatiana Radchenko (Lead Molecular Design S.L.)

Sweden

Hongming Chen (Astrazeneca)
Niclas Ståhl (University of Skövde)
Anna Tomberg (invited speaker) (AstraZeneca)

Switzerland

Stephen Chan (University of Basel)
Nadine Schneider (invited speaker) ( Novartis Institutes for BioMedical Research)
Francesca Grisoni (invited speaker) (ETH)

United Arab Emirates

Nawavi Naleem (New York University Abu Dhabi)

United Kingdom

Magd Badaoui (King”s College University)
Lucy Colwell (invited speaker) (University of Cambridge)
Marco Fiscato (invited speaker) (Benevolent AI)
Dimitar Hristozov (EVOTEC)
Adam Kells (Kings College London )
Alpha Lee (invited speaker) (University of Cambridge)
KOSTAS PAPADOPOULOS (Evotec UK Ltd)
Martin Rosellen (UCL)
Noor Shaker (invited speaker) (GTN Ltd)
Willem Van Hoorn (invited speaker) (Exscientia Ltd)

USA

Sathesh Bhat (invited speaker) (Schrodinger)
Tim Cernak (invited speaker) (University of Michigan)
Connor Coley (invited speaker) (Massachusetts Institute of Technology)
Zied Gaieb (University of California San Diego)
BYUNGCHAN KIM (Schrodinger LLC)
Guillaume Lamoureux (invited speaker)(Rutgers University)
Rita Podzuna (Schrodinger)
Adrian Roitberg (invited speaker) (University of Florida, Gainesville)

The agenda is available here