Dynamical systems with many degrees of freedom arise in a wide range of applications, including large scale molecular dynamics, climate and weather studies, and electrical power networks. The challenge in simulation is normally to extract statistical information, for example the average propensity of a given state of the system or the average time that elapses between certain events. Simulation data is easy to generate but often poorly utilized. The goal of this project is the development of a data-driven method for the automatic detection of a simplified description of the system based on a set of collective variables which can be used within efficient statistical extraction procedures. These slowest degrees of freedom are typically the most important ones. The dynamics are characterised as fluctuations in the vicinity of given state punctuated by relatively rare events describing transitions between the states. Efficiently identifying collective variables is the crucial first step in the design of coarse-grained models which can allow many order of magnitude increases in the accessible simulation timescale. By automatically finding collective variables, we can greatly simplify rapid study and comparison of many systems. The research builds on the technique of diffusion maps, whereby the eigenfunctions of a diffusion operator are used to characterise the metastable (slowly changing) states of the system. The potential impact of automatic coarse-graining will be felt most profoundly in fields such as rational drug design, where it is necessary to select specific drug molecules for their properties in interaction with some target, e.g. a protein. Bio-molecular simulation depends on the use of very specialised and intensely developed simulation codes which are the products of many years of development and government investment. In order to accelerate the implementation and testing of novel algorithms in this important area, this project includes a detailed plan for software development within the EPSRC-funded MIST (Molecular Integrator Software Tools) platform. Testing of the software methodology will be conducted via collaborations with chemists and pharmaceutical chemists, including researchers at Rice University (Houston, Texas) and Memorial Sloan Kettering Cancer Research Center (New York).

Biomolecular simulation is a fast growing area, making increasingly important contributions to structural biology and pharmaceutical research. Simulations contribute to drug development (e.g. in structure-based drug design and predictions of metabolism), design of biomimetic catalysts, and in understanding the molecular basis of disease and drug resistance. CCP-BioSim (ccpbiosim.ac.uk) was established in 2011 with support from EPSRC to strengthen molecular simulations at the life/sciences interface, and develop links with academia/industry. CCP-BioSim led in 2013 a successful EPSRC bid for a High-end Computing consortium in Biomolecular simulation, HECBioSim (hecbiosim.ac.uk). HEC-BioSim works to bring high-performance computing to a wider community of experimentalists and to engage physical scientists in biological applications. CCP-/HECBioSim regularly organize training workshops and provide a framework for networking and collaboration. We also work to develop and apply advanced methods, and engage with international activities (e.g. NSF, CECAM, NIH etc.). We actively engage with structural and chemical biologists and industrial researchers through collaboration, dissemination and application of software, and invitations to conferences and workshops. We actively collaborate with other CCPs via joint workshops and conferences. We actively support community software development and have released software to make biomolecular simulations more accessible to diverse communities. Our field benefits from continuous advances in HPC and chemical physics (e.g. multiscale modelling, 2013 Nobel Prize in Chemistry). Our techniques have reached a stage where we now aim to comprehensively transform the science of molecular design. Pharmaceutical companies continuously seek to design new drugs to treat e.g. antibacterial infections or cancers. Agrochemical companies continuously seek new chemicals to treat pests, supporting agricultural growth to secure food for our population. Biomolecular design is a complex multi-objective optimization problem. To make significant headways our field is increasingly combining multiple software packages into workflows. This departs from the historical paradigm of our field, where research problems were tackled with one or a few techniques at a time. Our community lacks software to easily assemble our tools into robust, scalable and comprehensive workflows needed to address the science of molecular design. As a CCP-/HECBioSim flagship community software project, we propose to develop BioSimSpace. Our software will provide an interoperability layer to allow software packages from our communities to work together. Translation tools will ensure that outputs from one package can be easily used as inputs to another package. Importantly, BioSimSpace will enable components of a workflow to be written such that are independent of the underlying software application. This will allow workflow components to be mixed and matched into more complex workflows, and for those workflows to select applications that will be optimal for the underlying computer hardware. We will use BioSimSpace to validate new workflows that address the grand challenges of screening drugs for potency, binding pathways and kinetics. By working with a commercial software vendor, we will make it easy to package BioSimSpace-based components so that they can be easily shared, installed and sold via a software marketplace. By working with a range of national and international industrial and academic partners, we will develop and apply BioSimSpace-based workflows to address molecular design problems faced by our community, and the pharmaceutical and agrochemical industries. By using supercomputers we will demonstrate how large BioSimSpace workflows help decrease the costs and time needed to design molecules for healthcare and industrial biotechnology applications.

Medical imaging has made major contributions to healthcare, by providing noninvasive diagnostics, guidance, and unparalleled monitoring of treatment and understanding of disease. A suite of multimodal imaging modalities is nowadays available, and scanner hardware technology continues to advance, with high-field, hybrid, real-time and hand-held imaging further pushing on technological boundaries; furthermore, new developments of contrast agents and radioactive tracers open exciting new avenues in designing more targeted molecular imaging probes. Conventionally, the individual imaging components of probes and contrast mechanisms, acquisition and reconstruction, and analysis and interpretation are addressed separately. This however, is creating unnecessary silos between otherwise highly synergistic disciplines, which our current EPSRC CDT in Medical Imaging at King's College London and Imperial College London has already started to successfully challenge. Our new CDT will push this even further by bridging the different imaging disciplines and clinical applications, with the interdisciplinary research based on complementary collaborations and new research directions that would not have been possible five years ago. Through a comprehensive, integrated training programme in Smart Medical Imaging we will train the next generation of medical imaging researchers that is needed to reach the full potential of medical imaging through so-called "smart" imaging technologies. To achieve this ambitious goal we have developed four new Scientific Themes which are synergistically interlinked: AI-enabled Imaging, Smart Imaging Probes, Emerging Imaging and Affordable Imaging. This is complemented by a dedicated 1+3 training programme, with a new MRes in Healthcare Technologies at King's as the foundation year, strong industry links in form of industry placements, careers mentoring and workshops, entrepreneurship training, and opportunities in engaging with international training programmes and academic labs to become part of a wider cohort. Cohort building, Responsible Research & Innovation, Equality, Diversity & Inclusion, and Public Engagement will be firmly embedded in this programme. Students graduating from this CDT will have acquired a broad set of scientific and transferable skills that will enable them to work across the different medical imaging sub-disciplines, gaining a high employability over wider sectors.