Grupo de Modelagem Molecular de Sistemas Biológicos @ LNCC

The GMMSB research group works with molecular modeling of biological systems, developing and applying computational methods in areas such as rational drug design and protein structure prediction. About us.




Publications

Click to see a list of our most recent papers.

Click here

Development

We develop own tools, check it out.

Click here

Supercomputing

SDmont offers high-performance computing.

Click here

Tools

The GMMSB group develops computer-aided drug design (CADD) and related methods, such as protein-ligand molecular docking and protein structure and function prediction.

Research

These are the group’s main research areas of interest.

Molecular Docking

Molecular docking methodologies are of great importance in the planning and design of new pharmaceutical drugs. The main objective of these techniques is to predict the experimental binding mode and binding affinity between two interacting molecules, usually a small ligand and a target protein of therapeutic interest. The GMMSB group works developing and applying protein-ligand and protein-protein docking methodologies.

Protein Structure Prediction

Protein structure prediction (PSP) has a large potential for valuable biotechnological applications and is one of the most important challenges in computational biology. Prediction itself encompasses a difficult optimization problem with thousands of degrees of freedom and is associated with extremely complex energy landscapes. In GMMSB group we mainly employ metaheuristics, such as Genetic Algorithms, and Machine Learning models for approaching this problem.

Machine Learning applied to Molecular Modeling

Machine learning (ML) has been increasingly applied to solve problems in several fields of study, including molecular modeling. With the ever-increasing availability of experimental data and general improvement in ML techniques, it is possible to tackle molecular modeling problems from a data modeling perspective. In GMMSB group, we apply ML techniques, such as deep learning, to create models that help to answer questions and develop tools in fields such as protein structure prediction and binding affinity estimation.

Quantum Mechanics for Molecular Modeling

Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus msdus.

Publications

A list of selected publications from the GMMSB group.

“Design, synthesis, cholinesterase inhibition and molecular modelling study of novel tacrine hybrids with carbohydrate derivatives.” Bioorganic & medicinal chemistry (2018). DOI: 10.1016/j.bmc.2018.10.003

“Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges.” Frontiers in pharmacology (2018). DOI: 10.3389/fphar.2018.01089

“Design, synthesis and pharmacological evaluation of N-benzyl-piperidinyl-aryl-acylhydrazone derivatives as donepezil hybrids: Discovery of novel multi-target anti-alzheimer prototype drug candidates.” European journal of medicinal chemistry (2018). DOI: 10.1016/j.ejmech.2018.01.066

“Discovery of naphthyl‐N‐acylhydrazone p38α MAPK inhibitors with in vivo anti‐inflammatory and anti‐TNF‐α activity.” Chemical biology & drug design (2018). DOI: 10.1111/cbdd.13085

“Chiral bistacrine analogues: synthesis, cholinesterase inhibitory activity and a molecular modeling approach.” Journal of the Brazilian Chemical Society (2017). DOI: 10.21577 / 0103.5053.20170074

“Design, synthesis and evaluation of novel feruloyl-donepezil hybrids as potential multitarget drugs for the treatment of Alzheimers disease.” European journal of medicinal chemistry (2017). DOI: 10.1016/j.ejmech.2017.02.043

“Critical features of fragment libraries for protein structure prediction.” PloS ONE (2017). DOI: 10.1371/journal.pone.0170131

“Improving de novo protein structure prediction using contact maps information.” 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). DOI: 10.1109/CIBCB.2017.8058533

“Genetic operators based on backbone constraint angles for protein structure prediction.” 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). DOI: 10.1109/CIBCB.2015.7300285

“A multiobjective approach for protein structure prediction using a steady-state genetic algorithm with phenotypic crowding.” 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). DOI: 10.1109/CIBCB.2015.7300284

“Receptor–ligand molecular docking.” Biophysical reviews (2014). DOI: 10.1007/s12551-013-0130-2

“Structural modeling and docking studies of ribose 5-phosphate isomerase from Leishmania major and Homo sapiens: a comparative analysis for Leishmaniasis treatment.” Journal of Molecular Graphics and Modelling (2015). DOI: 10.1016/j.jmgm.2014.11.002

“A multiple minima genetic algorithm for protein structure prediction.” Applied Soft Computing (2014). DOI: 10.1016/j.asoc.2013.10.029

“A dynamic niching genetic algorithm strategy for docking highly flexible ligands.” Information Sciences (2014). DOI: 10.1016/j.ins.2014.08.002

Team

The GMMSB group is formed by a team of muldisciplinary researchers from areas such biology, computer science, physics and chemistry.

Permanent Researchers




Person1

Laurent Dardenne

PhD in Biophysics. Group Leader.

Has a Bachelor and MSc degree in Physics and a PhD in Biophysics. Chief of the GMMSB research group.

Person3

Helio Barbosa

PhD in Civil Engineering

Has Bachelor, MSc and PhD degree in Civil Engineering. Currently is an Associate Professor @ UFJF and Senior Researcher @ LNCC. Works mainly with Numerical Methods, Metaheuristics, Optimization, Structural Mechanics and Molecular Modeling.

Person3

Fábio Lima Custódio

PhD in Computational Modelling

Has Bachelor degree in Biological Sciences and PhD in Computational Modelling. Currently works in the Department of Computational Mechanics @ LNCC. Works mainly with Protein Structure Prediction and Machine Learning applied to Molecular Modeling.

Post-Doctoral Researchers




Person3

Gregório Kappaun

PhD in Computational Modelling

Has a Bachelor’s degree in Biological Sciences (UENF, 2008). Received his MSc degree in Computational Modeling with emphasis in Bioinformatics (2011) and his DSc degree in Computational Modeling of Biological Systems (2015) from the National Laboratory for Scientific Computing (LNCC). Acts in the area of Molecular Modeling of Biological Systems, having experience in Molecular Dynamics, Bioinformatics, Protein-Solvent Interaction, Genetic Algorithms, Multiobjective Optimization and mainly in the development of strategies for Protein Structure Prediction.

Person3

Isabella Alvim Guedes

PhD in Computational Modelling

Person3

Karina Baptista dos Santos

PhD in Computational Modelling

D.Sci. Students




Person3

Emerson Correia

PhD student

Has Bachelor degree in Computer Science and MSc degree in Computational Modelling. Is currently a PhD student in Computational Modelling. Works mainly with protein structure prediction, molecular dynamics, machine learning and evolutionary computing.

Person3

Ana Luiza Karl

PhD student

Has Bachelor degree in Biomedical Sciences and MSc degree in Computational Modelling. Is currently a PhD student in Computational Modelling and an undergrad student in Information Technology. Works with molecular modeling and protein flexibility representations in docking methodologies.

Person3

Aaron Leão

PhD student

Person3

Paulo Werdt

PhD student

M.Sci. Students




Person2

Matheus Müller

MSc student

Has a Bachelor degree in Biotechnology (UFRJ) and is currently an MSc student in Computational Modelling, studying molecular modeling and machine learning models applied to binding affinity prediction.

Person3

Lincon Vidal

MSc student

Has a Bachelor degree in Biotechnology and is currently an MSc student in Computational Modelling, studying molecular modeling, energy landscape theory and machine learning models applied to binding affinity prediction.

Person3

Nicolau Gonçalves

MSc student

Person3

Ronniery Pereira

MSc student

External Collaborators




Person3

Priscila Capriles

PhD in Computational Modelling

Person3

Raphael Trevizani

PhD in Computational Modelling

Person3

Eduardo Krempser

PhD in Computational Modelling

Person3

Douglas Adriano Augusto

PhD in Civil Engineering

About us

The GMMSB (Molecular Modeling of Biological Systems Group) is a multidisciplinary research group at National Laboratory for Scientific Computing (LNCC) of the Ministry of Science, Technology, Innovation and Communications (MCTIC). The group is located in the city of Petrópolis, a mountain city in Rio de Janeiro state. Since 2002, the group works with biology, physics, applied mathematics and high-performance computing. The main objectives of GMMSB are the development and application of computational methods, techniques and algorithms in rational drug design, ab initio protein structure prediction and quantum calculations of biological macromolecules electrostatic properties. The group has an important actuation in the LNCC Computational Modeling Multidisciplinary Post-graduation Program (Master’s and Ph.D degrees), with students from biology, chemistry, physics, mathematics, computation and engineering sciences.
Our partner institutions: FIOCRUZ/IOC, LASSBIO/UFRJ.

EMMSB

The group has organized several Schools of Molecular Modeling of Biological Systems (EMMSB), from 2002 until now (biannually). The EMMSB is an academic event focused in areas such as computational molecular modeling, molecular dynamics, drug development, bioinformatics, quantum calculations applied to biological systems and more. The event also counts with lectures from renowned researchers, practical and theoretical workshops and student presentations. These schools had a national impact and were organized in collaboration with IBCCF/UFRJ and FIOCRUZ/MS. Join us in the next edition!

9 Organized Schools

Get in touch

  • Av. Getulio Vargas, 333 - Quitandinha, Petrópolis - RJ - Brazil
    Laboratório Nacional de Computação Científica (LNCC)
  • +55 24 2233 6009
  • lncc.br