Special Interest Group DRUG-DESIGN

Program

BRASÍLIA TIME = GMT-3

Opening Words

Lightning talk 1: "Virtual Screening of Substances with Potential Antiviral Activity Against Three Flaviviruses: Dengue Virus, Yellow Fever Virus and Zika Virus" by Mateus Serafim

Lightning talk 2: "Phenotypic Screening of Compounds Enriched by Molecular Docking to Protein Kinase Targets in Schistosoma Mansoni" by Naiara Clemente Tavares

"Targeting proteases to treat neglected and emerging diseases" by Rafaela Ferreira

Lightning Talk 3: "Arg2 Snps Associated with Hbf Response in Patients Sickle Cell Anemia Treated with Hydroxyurea" by Bárbara Nogueira

Lightning Talk 4:"Construction of a Nanoparticle Based on a Synthetic Virus-like Protein with Chemotherapy Potential" by Amanda Patrícia Gonçalves

"Molecular intervention of SARS-CoV-2 drug targets as potential therapy for COVID-19" by Neera Borkakoti

Lunch Break

"Data fusion in drug-target interaction prediction for drug repositioning" by Daniele Parisi

Lightning talk 5: "Metformin Regulates Cells Epigenomic Landscape Leading to Decreased Proliferation and Inflammation in Hepatocytes" by Izabela Conceição

Lightning Talk 6: "In Silico Approaches for Mycoplasma Pneumoniae Multi-Epitope Vaccine Construction" by Thaís Cristina Rodrigues

"Network Medicine approaches for the study of SARS-CoV2 and drug repurposing" by Deisy Morselli Gysi

Closing Words

CONFIRMED SPEAKER

Neera Borkakoti
Wellcome Genome Campus, UK
Visiting Scientist - EMBL-EBI
Molecular intervention of SARS-CoV-2 drug targets as potential therapy for COVID-19

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. I will present here a set of deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. Even though all methods have high predictive power, the consensus of those methods increase the hit rate to 62%, in contrast to the usual 0,8% when non targeted screenings. We also find that 76 of the 77 drugs that successfully reduced viral infection in VeroE6 cells do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a novel methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

CONFIRMED SPEAKER

Deisy Morselli Gysi
CCNR - Northeastern University, USA
Postdoctoral Researcher Associated
Network Medicine approaches for the study of SARS-CoV2 and drug repurposing

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. I will present here a set of deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. Even though all methods have high predictive power, the consensus of those methods increase the hit rate to 62%, in contrast to the usual 0,8% when non targeted screenings. We also find that 76 of the 77 drugs that successfully reduced viral infection in VeroE6 cells do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a novel methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.

CONFIRMED SPEAKER

Rafaela Salgado
Universidade Federal de Minas Gerais, Brazil
Associate Professor - ICB
Targeting proteases to treat neglected and emerging diseases

R. Salgado is Ph.D. in Chemistry and Biological Chemistry from University of California San Francisco, USA. Since 2011, she is affiliated to the Department of Biochemistry and Immunology at UFMG, where she applies computational techniques and experimental assays to development of new ligands and therapeutic targets. Recently, she was awarded with the prizes: “L’Oréal-UNESCO-ABC Para Mulheres na Ciência 2017, Categoria Química” and “L’Oréal-UNESCO For Women in Science International Rising Talent 2018”.

CONFIRMED SPEAKER

Daniele Parisi
Yale School of Medicine, USA
Postdoctoral Associate - Immunobiology
Data fusion in drug-target interaction prediction for drug repositioning

Identifying drug-target interactions is a crucial step in drug repositioning, the process of suggesting new indications for known drugs. There are about 9000 FDA-approved and experimental small molecule drugs and more than 500.000 protein records available. Performing in vitro experiments would be too expensive and time-consuming to check all the putative drug-target couples, therefore computational techniques might help to predict compound biological activity (IC50) and suggest new putative medical indications for existing drugs. Machine learning techniques such as Bayesian matrix factorization and deep neural networks can integrate structural information of drugs, proteins and their binding to better predict biological activity and suggest new drug-target interactions, with a big impact on the drug discovery process. Different kinds of side information can be used to help the prediction process, such as chemical structures of the drugs, 3D structures of the protein targets or phenotypic effect of drug-target interactions. In my work I analysed the contribution brought by different kinds of heterogeneous information in the prediction process, taking into account different modalities of validation, as well as advantages and difficulties related to the application of each specific type of data.