CADD approaches

Molecular modeling is a foundation of computer aided drug design (CADD). Initially naive and oversimplified approaches introduced over half a century ago, gradually matured into sophisticated, fully-estabilished tools. In addition to scientific advances, these tools nowadays benefit from the availability of extensive datasets, substantial computational resources, and proliferation of machine learning techniques. Consequently, CADD is now being widely used to accelerate and optimise drug design process across all its stages.

We specialize in the following aspects of molecular modelling:

• biophysical descriptors: 2D, 3D, and physicochemical descriptors, molecular fingerprints
• QM calculations for conformational analysis and force field parameterization of drug-like molecules,
• target protein modelling: homology modelling and refinement of protein structures, binding site annotation, hydration analysis, simulations of protein dynamics & conformational variability,
• lead compound optimisation: simulation-based free energy methods for absolute and relative affinity assessment.

Virtual High-Throughput Screening (vHTS) aims to predict the biological activity of a large number of compounds against specific targets or pathways based on computer simulation rather than in a physical laboratory setting. Two typical scenarios: structure- or ligand-based vHTS, assume either the knowledge of the target receptor 3D structure or the existence of possibly wide library of small molecules with already measured activity against the desired end-point.

• structure-based vHTS: given 3D model of the receptor (eg. crystallographic of cryo-electron microscope structure) specialised computer software is employed to predict plausible binding modes of the considered compounds as well as their ranking according to calculated binding free energy. We offer an extensive expertise in such docking & scoring campaigns, including:
  • assessment and refinement of the receptor model,
  • flittering and standardisation of small molecules libraries,
  • selection of suitable docking protocol,
  • critical evaluation and interpretation of the results.

• ligand-based vHTS: based on the group of molecules whose biological activity in a certain aspect has been quantified, a mathematical relationship is established between their structural and physicochemical properties and the magnitude of the effect under study. The model is subsequently used to estimate the activity of a large pool of screened compounds. Our expertise in such Quantitative Structure-Activity Relationship (QSAR) modelling includes:
  • selection and calculation of relevant molecular descriptors,
  • formulation of QSAR models using state of the art machine learning approaches,
  • thorough evaluation of the results.

Evaluation of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties for drug candidates is essential at early stages of development to limit the risk of potential safety and efficacy issues. Calculation of ADMET profile based on molecular structures is a fast and cost efficient method allowing to filter out high-risk compounds from the dataset. We offer the following approaches to the evaluation of ADMET properties:

• descriptor-based toxicity assessment, which relies on machine learning models trained to predict a range of toxicity endpoints based on molecular fingerprints,
• target-based toxicity assessment, which uses molecular docking and structural modeling to assess how a molecule interacts with biological targets and infer potential toxicity mechanisms,
• Physiologically-Based Pharmacokinetic (PBPK) modelling, which takes into account the physiological processes and organ-specific characteristics to construct a mathematical model for predicting drug concentration-time profiles within different body compartments.