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Der Pharmacia Sinica
ISSN: 0976-8688
Eurosc i con Conference on
Medicinal Chemistry
and Biosimilars
M a r c h 2 5 - 2 6 , 2 0 1 9
B u d a p e s t , H u n g a r y
Medicinal Chemistry & Biosimilars 2019
Despite significant advances in our understanding of the biological basis
of diseases, pharmaceutical R&D is struggling to sustain the level of
productivity and efficiency it reached in the second half of the 20th century.
High failure rates and the increasing cost of drug discovery as well as
extended research and development timelines hinder the development of
medicines. Due to these challenges there has been an increasing need for
substantial innovations in the pharmaceutical sector. It has been shown that if
the selection of the synthetic targets in lead optimization cycles is supported
by QSAR or deep learning methods, the number of compounds synthesized as
well as the cycle time for each iteration can be significantly reduced. We have
developed a rule-based artificial intelligence technology that can produce a
large number of novel and synthetically-enabled lead analogues and scaffold
hopping designs around lead structures. Since its introduction, the cloud-based
SynSpace software has been found by multiple organizations to generate a
larger number of relevant novel ideas around leads than medicinal chemist
teams can do. Thus, SynSpace is a valuable addition to the medicinal chemistry
toolbox. We have also been developing automated lead analysis tools that-in
conjunction with SynSpace-can automatically carries out scaffold hopping and
lead analogue idea generation and thereby offer large sets of novel and project
specific lead-like structures to advanced AI platforms for selection. These
platforms have the biggest impact on a number of key parameters in drug
discovery: cycle time, number of discovery cycles, the number of compounds
to be synthesized and coverage of IP space. Improvements in these factors can
be converted into higher success rates and major resource savings towards a
more economical and productive candidate development phase.
Biography
Gergely Makara has completed his PhD in medicinal chemistry
at SUNY at Buffalo in 1996 and his postdoctoral studies in
medicinal chemistry and molecular modelling with Garland
Marshall at the Center for Molecular Design at Washington
University at St. Louis in 1998. Since then he has spent 20 years
in the pharmaceutical industry, most of it in leadership levels
at Neogenesis Pharmaceuticals (Boston, USA), Merck & Co.
(Rahway, USA), AMRI Hungary (Hungary), ComInnex (Hungary)
and ChemPass (Hungary). His expertise includes organic
synthesis, medicinal chemistry, fragment-based drug discovery,
drug design, and cheminformatics. He has publishedmore than
30 papers in reputed journals and has contributed to 10 patent
applications.
SynSpace: a design platform to expand synthetically-enabled
scaffold and lead analogue space for medicinal chemistry
and AI-assisted drug discovery
Gergely Makara, Gabor Pocze, Laszlo Kovacs, Anna Szekely
and Istvan Szabo
ChemPass Ltd, Hungary
Gergely Makara et al., Der Pharmacia Sinica 2019, Volume:10
DOI: 10.21767/0976-8688-C1-002