In Silico
Research conducted using computer simulations and computational modelling rather than physical laboratory experiments. In silico methods are increasingly used in peptide drug discovery for predicting receptor binding, simulating molecular dynamics, and screening virtual peptide libraries.
Technical Context
Computational methods in peptide drug development include: molecular docking (predicting peptide-receptor binding modes and binding energy), molecular dynamics (MD) simulations (simulating peptide conformational behaviour in solution over nanosecond-microsecond timescales), structure-activity relationship (SAR) modelling (predicting how sequence modifications affect activity), pharmacophore modelling (identifying key structural features required for receptor binding), ADME prediction (computational models for absorption, distribution, metabolism, excretion), and machine learning/AI (training models on peptide activity datasets to predict properties of novel sequences). AlphaFold (protein structure prediction) has accelerated understanding of peptide-receptor interactions. In silico methods reduce experimental screening burden by prioritising the most promising candidates for synthesis and testing. However, computational predictions always require experimental validation.