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May 16, 2026Sensors0 citationsOpen Access

Biophysical Sensing Tools in Drug Discovery: Integrating Kinetics, Thermodynamics, Cellular Target Engagement and Structure

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EBEsra BalıkçıCACaner Akıl

Key Points

  • The aim is to highlight the role of biophysical sensing tools in accurately characterizing ligand-target interactions during drug discovery.
  • Survey of major biophysical sensors used in drug discovery.
  • Discussion of measurement principles and parameters from various biophysical methods.
  • Integration of biophysical data with structural biology and computational analysis.
  • Biophysical sensing technologies provide direct measurements of interactions, enhancing confidence in binding data.
  • Orthogonal methods improve assay reliability and decision-making in drug development processes.
  • Integration of these measurements with structural analysis aids in understanding molecular recognition.

Abstract

Biophysical sensing technologies have become central to modern drug discovery because they enable direct, quantitative characterization of ligand–target interactions. In contrast to conventional biochemical and cellular assays that infer binding from downstream functional responses, biophysical methods detect interaction events through measurable physical changes such as refractive index, heat, fluorescence, mass, or protein stability. This review surveys the major classes of biophysical sensors used in drug discovery, including surface-based optical methods, calorimetry, solution-state spectroscopic techniques, mass spectrometry-based approaches, and cellular target engagement assays. For each modality, we outline the measurement principle, the key parameters obtained, and its value across hit identification, hit validation, lead optimization, and mechanism-of-action studies. We also emphasize the growing importance of combining orthogonal methods to improve confidence in binding data, resolve assay artifacts, and strengthen early decision-making. Finally, we discuss how biophysical measurements are increasingly integrated with structural biology and computational analysis to support more predictive and mechanism-driven discovery workflows. Collectively, these technologies provide a richer and more reliable understanding of molecular recognition and thereby improve the progression of drug candidates.

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Cite This Study

Balıkçı et al. (2026) studied this question.

synapsesocial.com/papers/6a0809f1a487c87a6a40bd49https://doi.org/10.3390/s26103105
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