Severe ocular disorders are affecting the lives of more than 100Mill people world-wide and at least 25% of the population above 70 years of age, a growing demographic group in EU. More than 8 million people lose their lives to cancer every year, making cancer a leading cause of pre-mature mortality in the world. The main hallmarks of severe eye conditions (i.e angiogenesis, inflammation and vascular permeability) play also pivotal roles in cancer, being therapeutic targets to treat both kind of diseases. The overall goal of 3D-NEONET is the improvement of available treatments for cancer and ocular disease by enhancing drug discovery-development and delivery to targeted tissues, through advanced international co-operation between academic and non-academic partners. The interdisciplinary expertise provided by 18 partners in 7 countries encompasses among others: drug screens, ADME, toxicology, preclinical models, nanotechnology, biomaterials and clinical trials. After the success with ongoing FP7-IAPP project 3D-NET (Drug Discovery and Development of Novel Eye Therapeutics; (www.ucd.ie/3dnet), we are assembling 3D-NEONET, this enlarged European interdisciplinary consortium that will join forces and exchange skills to enhance current therapies in oncology and ophthalmology. The 3 global objectives of 3D-NEONET are: 1- Enhance the discovery and development of novel drugs, targets and biomarkers for ophthalmology and oncology. 2- Improve the Delivery of Therapeutics for Oncology and Ophthalmology 3- Enhancement of Research, Commercial and Clinical Trial Project Management Practices in these fields. Through participation in the program, 3D-NEONET is the vehicle for driving synergies between academic and non-academic partners leading to increased scientific and technological excellence as well as tangible innovative outputs that will strengthen the competitiveness of both the researchers and industries of the network even beyond the lifetime of the network.

Atomistic simulations are the main application of high-performance computing research, and increasingly underpin innovative R&D processes in the chemical and life sciences industry. OpenMM is the fastest growing atomistic simulation engine among the current ecosystem of open-source academic software. Originally targeting a biomolecular simulation audience, the OpenMM user base is growing exponentially and has permeated diverse related domains, including materials modelling, quantum chemistry, structural bioinformatics, chemoinformatics, artificial intelligence and machine learning. The success of OpenMM is down to a design that achieves an excellent tradeoff between extensibility (via a robust user interface) and performance on GPUs (via auto generated CUDA kernels) for molecular dynamics (MD) simulations. OpenMM is used standalone or via plugins to other atomistic simulation engines, providing access to GPU-accelerated MD simulation capabilities for the whole atomistic simulation ecosystem. We have surveyed the OpenMM user community to identify its most pressing needs. OpenMM is currently maintained by a single core developer who can no longer support the training and support needs of its rapidly growing user community. We will transition OpenMM to a more sustainable community-driven development model. We will develop training resources to upskill users, and engage the community to widen participation in developing and maintaining OpenMM functionality. Machine learning (ML) potentials have the potential to revolutionise the future of atomistic simulation methodologies. Our community survey has identified strong interest in ANI neural network and GAP Gaussian process regression methods. We will deliver a self-contained GPU-optimised GAP implementation in OpenMM and coordinate with project partners working on an OpenMM ANI implementation to offer the community a library of ML potentials that can be readily plugged into existing simulation engines. OpenMM must adapt to scientific (ML potentials) and technological (increased hardware heterogeneity) drivers to continue offering its user base an optimised tradeoff between speed and ease of modification over the coming decade. We will integrate in OpenMM a multiple level intermediate representation compiler (MLIR) to auto generate from user-specified Python instructions optimised low-level code targeting diverse hardware. By enabling users to specify custom atomic featurisation techniques as OpenMM operations, which can be finely interleaved with Tensorflow or Pytorch operations, we will position OpenMM as the simulation engine of choice to support deployment of next generation ML potentials onto current GPUs and emerging AI-hardware accelerators. Our community has also required support to facilitate the combined use of independently developed OpenMM software solutions with other software from the broader atomistic simulation ecosystem. This research will develop a standardised interface to integrate OpenMM community software with CCPBioSim's interoperable Python framework BioSimSpace. We will demonstrate integration of all the work packages of this research via production of GAP ML pipelines for two use cases that target grand challenges in soft-condensed matter modeling (organocatalysis - recently recognised by the 2021 Nobel Prize in Chemistry- and protein-ligand binding). Altogether this research will position the OpenMM user community at the forefront of next-generation hybrid machine learning/molecular mechanics potentials for soft-condensed matter modelling. Deeper integrations with AI and HPC communities will pave the way for atomistic simulations to harness emerging exascale opportunities. Transitioning from a single developer to a community-driven development governance model will improve sustainability of the codebase and encourage greater adoption of OpenMM in associated academic communities and industry.

Advances in High Performance Computing (HPC) and scientific software development will have increasingly significant societal impact through the computational design of new products, medicines, materials and industrial processes. However, the complexity of modern HPC hardware means that scientific software development now requires teams of scientists and programmers to work together, with different and non-overlapping skill-sets required from each member of the group. This complexity can lead to software development projects stalling. Investments in software development are in danger of being lost, either because key members of a team move on, or because a lack of planning or engagement means that a sustainable user and developer community has failed to gel around a particular code. Research Software Engineers (RSEs) can solve this problem. RSEs have the skills and training necessary to support software development projects as they move through different stages of the academic software lifecycle. Academic software evolves along this lifecycle, from being a code used by an initial team of researchers, through to a large multi-site community code used by academics and industrialists from across the UK and around the World. RSEs provide the training and support needed to help academic software developers structure their projects to support the sustainable growth of their user and developer communities. RSEs are also highly skilled programmers who can train software developers in advanced HPC techniques, and who can support developers in the implementation, optimisation and testing of complex and intricate code. Together with academic software developers, RSEs can support UK investment in HPC, and ensure that the potential of computational science and engineering to revolutionise the design of future products and industrial processes is realised. This project aims to develop sustainable RSE career pathways and funding at Bristol. This will support the growth of a sustainable team of RSEs at the University. Software development projects that will be supported include; the building of code to interface real biological cells with virtual simulated cells, so to support the rapid design of new biomanufacturing control processes; the development of code to more quickly model the behaviour of electrons in novel materials, to support the design of new fuel cells and batteries; code to improve our understanding of glass-like matter, so to help design new materials with exciting new properties; software to support modelling of the quantum interaction between laser light and microscopic nanoparticles, to support the design of optical tweezers and new optically driven nanomachines; and code to design new medicinal drugs and to understand why existing treatments are no longer working, thereby supporting the development of 21st century medicine. Finally, this project aims to create a coherent set of teaching materials in programming and research software engineering. These, together with the development of software to support science and programming lessons held in an interactive 3D planetarium, will help inspire and educate the next generation of scientists and RSEs. These materials will showcase how maths, physics, computing and chemistry can be used in the "real world" to create the high-tech tools and industries of the future.

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.

The availability of crystallographic information for protein targets of pharmaceutical interest has dramatically increased in the recent years, even for those targets, such as trans-membrane receptors and ion channels, which until recently were considered extremely hard to crystallise. The availability and cost per calculation of high-performing computing through cloud-based infrastructure has also dramatically improved in the recent years. This has made the application of complex drug design methods and algorithms to the study of target-ligand interactions accessible to many more pharmaceutical researchers. Cresset have an existing set of products aimed at helping both computational and medicinal chemists with the design of molecules, calculating the 3D properties of molecules and their interactions with target proteins. There is a constant desire for new tools and scientific methods to facilitate the design process. As new methods are often highly compute intensive, these are ideally presented to researchers through cloud computing. We have carried out market research in this area and have received very positive feedback on our concept for a new software application that integrates cloud resources with a traditional interactive GUI for structure-based design. The market feedback also indicates that a cloudbased platform will become increasingly valuable over the next few years as a means of providing rapid access to novel science. Our research indicates that an application that combines this novel architecture with cutting edge science will provide a paradigm shift in the speed of new molecule design and in the exploitation of available crystallographic information on protein targets of pharmaceutical interest.