InSiliChem is a molecular modeling group with expertise in a wide variety of methods ranging from homology modeling, molecular dynamics, protein-ligand docking or Quantum Mechanics-based methods. To support our research, the group also deploys substantial efforts to develop new software such as GaudiMM, BioMetall or Talaia.
InSiliChem studies address a variety of different modeling questions. From sequence analysis, protein-ligand docking to hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) methods, InSiliChem has the expertise to work on very different systems. Our application areas include the study of biological mechanisms related to the interaction of proteins with metals, the study of the therapeutic bases of natural compounds, drug discovery and enzyme design.
Due to our research interest, we also perform methodological development including new software.
Our main research lines are 1) computer-based molecular design and 2) interactions of exogenous compounds with living organisms. In both lines, most activities focus on metal-containing systems for which the team has been gaining experience over the years.
The usefulness of molecular modeling in many fields of chemical sciences is not something to be demonstrated. Modeling allows to capture molecular knowledge that allows chemists, biochemists and nanotechnologists to make more solid hypotheses about the molecular nature of their systems and to develop them from these. Our group has a large part of its activity dedicated to helping in the design of new molecular entities ranging from artificial metalloenzymes to new drugs. The particularity of the group is to give priority to metal systems.
The interaction of exogenous compounds with living organisms is key to many of their functions. It can also be the source of therapies and diseases. The second main line of research of the group focuses on predicting at the molecular level the key interactions between exogenous compounds and their targets.
We focus our activities on two categories: metal ions (whether beneficial or contaminant) and natural compounds of pharmacophea. We focus on decoding processes involving transition metals or organic compounds and related diseases of neurodegenerative, metabolic, or pathogenic origin such as malaria, dengue and, more recently, covid-19.
Information of interest
Computational BioNanoCat. Marechal, J. Convocatòria SGR 2021 (Ref. 2021SGR00019). Direcció General de Recerca de la Generalitat de Catalunya. Execution: 1/01/22 → 30/06/25.
CypTox: Training next level scientists and researchers to develop highly selective and safe insecticides. Marechal, J (Pl). European Commission (CE), Ref. H2020-101007917-CypTox. Execution: 1/01/21 → 30/06/25.
FRONTERAS EN LA MODELIZACION DE PROCESOS DE CATALISIS Y RECONOMIENTOS MOLECULARES MEDIADOS POR METALES DE TRANSICION. Marechal, J (PI). Convocatòria Proyectos Generación del Conocimiento (Ref. PID2020-116861GB-I00). AEI, Ministerio de Ciencia e Innovación (MICINN). Execution: 1/09/21 → 31/03/25.
BIOINORGANICA COMPUTACIONAL: DESDE LA BIOCATALISIS SOSTENIBLE HASTA NUEVAS ESTRATEGIAS EN QUIMICA MEDICA. Marechal, J (PI). Convocatòria Proyectos Generación del Conocimiento (Ref. PID2023-149492NB-I00). AEI, Ministerio de Ciencia e Innovación (MICINN). Execution: 1/09/24 → 31/12/27.
Nat. Catal., 2022 (Q1, IF: 40.706). Design and evolution of chimeric streptavidin for protein-enabled dual gold catalysis. F. Christoffel, et al.
Chem. Comm., 2021 (Q1, IF: 6.22). Selective recognition of A/T-rich DNA 3-way junctions with a three-fold symmetric tripeptide. J. Gómez-González, et al.
PCCP, 2021 (Q1, IF: 3.430). Impact of Cu(II) and Al(III) on the conformational landscape of Amyloidβ 1-42. L. Roldán-Martín, et al.
JCIM, 2021 (Q1, IF: 4.549). BioMetAll: Identifying Metal-Binding Sites in Proteins fromBackbone Preorganization. J.E. Sánchez-Aparicio, et al.
Acc. Chem. Res., 2020 (Q1, IF: 21.661). Molecular Modeling for Artificial Metalloenzyme Design and Optimization. L. Cotchico, et al.
Int. J. Mol. Sci, 2019 (Q1, IF: 4.556). GPathFinder: Identification of Ligand-Binding Pathways by a Multi-Objective Genetic Algorithm. J.E. Sánchez-Aparicio, et al.
