Publications by authors named "Alexis Molina"
Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques.
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- Direct-coupling analysis (DCA) has been adapted into RADI/raDIMod to predict protein structures by grouping residues into two categories: polar and non-polar, based on their chemical properties.
- This method simplifies the analysis and reduces the number of sequences needed for alignment while still capturing essential features of protein folding due to hydrophobic forces.
- RADI and raDIMod deliver accurate protein conformations, especially when more than 30-50% of the sequence can be modeled using super-secondary structure motifs, demonstrating that different residue groupings can lead to similar structural predictions.