Drug discovery and development is an expensive and time consuming process due to the high costs of R&D and clinical trials. New drug R&D involves the identification of a target, such as a protein or enzyme and the discovery of some appropriate drug candidates that can block or activate the target. Clinical testing is the most extensive and expensive phase in drug development and is done in order to obtain the necessary governmental approvals.
Drug discovery is a multi-objective problem, something the KPF is specifically designed for. The KPF can generate models of targets and candidates that incorporate varying degrees of functionality allowing many aspects of their interaction to be modelled and considered e.g. electrostatic forces, structure characteristics, docking (ligand and receptor) configurations, quantum mechanical methods, various scoring functions and the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs.
Combined with our High Performance Computing (HPC) technology we can significantly accelerate the drug discovery process and provide more detailed and accurate modelling, simulation and analysis. As the KPF can be operated in supervised mode, semi-supervised mode and unsupervised mode, users can design molecules from scratch and or modify existing ones generated from libraries.
Key advantages are establishing more promising hits and hence more promising lead compounds and the ability to easily modify candidate structures to achieve more favourable interactions and reduce side-effects. The versatile and scalable nature of the KPF allows it to be used to implement a range of drug discovery methodologies:-
- Virtual Screening – Ligand and Structure based.
- Pharmacophore modelling
- Molecular docking – binding modes, pose and ranking analysis
- Quantitative structure–activity relationships (QSAR) techniques
- Quantum mechanical (QM) methods due to high accuracy is required to estimate (relative) binding affinities
- Quorum sensing