Molecular Complexity
What is Molecular Complexity?
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The Quantitative Complexity Theory (QCT) bridges physics and information theory providing the first dynamic molecular complexity metric which quantifies the amount of information encoded in a particular structure. Analyzing the dynamics of atomic interactions in a molecule, QCT pinpoints “complexity hotspots”/potential pharmacophores, which drive its biological function.
Molecular complexity is computed via a proprietary algorithm. By understanding the complexity of a molecule in the context of its dynamics, new knowledge and insights may be obtained. Conventional, “static” metrics are unable to quantify complexity correctly as it is not a constant. In fact, molecular complexity is impacted by external factors such temperature, pressure, or solvent (viscosity, pH), etc. This means that depending on the conditions, a molecule may exhibit a more or less complex behavior.
Why is Molecular Dynamics so Important?
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The biological and chemical properties of a molecule are fundamentally the expression of its dynamics. Molecules are dynamic, vibrating ensembles of atoms and their dynamics is not simply a background process; it is the very essence of information transmission and of biological function.
Our physics-based approach provides new insights into the intricate dynamics of molecules, identifying complexity hotspots, i.e., the regions and moieties that drive biological activity, indicating where to focus lead optimization. This can accelerate the pre-clinical trials phase by 50%-to 75%.
What makes BioDynLab different?
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Current compound selection relies on synthesizability scoring and medicinal chemistry expertise rather than deterministic guidance for achieving optimization targets. This drives Chemical Space Exploration Through Trial-and-Error—Without systematic prioritization, medicinal chemists must synthesize and test numerous analogs to identify successful modifications.
BioDynLab’s approach is deterministic and it distinguishes atoms essential for activity from those suitable for modification to improve ADMET properties without compromising potency. This is accomplished without the use of Machine Learning, meaning it is fast and bias free. Moreover, it offers 100% explainability.
Can BioDynLab’s Approach be used to Analyze Proteins?
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Analyses of the GLP-1 peptide configurations (Byetta, Semaglutide, and Tirzepatide) through molecular dynamics simulations identifies critical amino acid hotspots that drive receptor binding and biological activity.