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This year 15 interns join us for a 10 week programme between 28th June and 25th September 2021. The projects they are working on are interdisciplinary and include both cutting-edge AI and cutting-edge chemical discovery and demonstrate how and why they are relevant to AI3SD. The projects must be able to demonstrate valuable outputs both with respect to developing student skill and providing impact to AI3SD.The Interns will be required to produce a poster for the AI3SD Summer Project Symposia 1st – 2nd September 2021, and they will take part in our Skills4Scientists programme that will run weekly across July and August whereby the interns will be given training in a range of research, technical and interpersonal skills, alongside informative career-based events.

This video forms part of the Skills4Scientists Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Physical Sciences Data-Science Service (PSDS). This series ran over summer 2021 and aims to educate and improve scientists skills in a range of areas including research data management, python, version control, ethics, and career development. This series is primarily aimed at final year undergraduates / early stage PhD students. This video is a compilation of posters presented at the Skills4Scientists Posters & Careers Symposium. These poster presentations are predominantly from summer students involved in the AI3SD 2021 summer internship program. Higher resolution versions of the posters are available on the poster symposium website: https://www.ai3sd.org/s4s-symposium20...Not all poster presenters requested a recording of their talk. The following posters recordings are included in this compilation video. Poster 1 - Nearer the nearsightedness principle: Large-scale quantum chemical calculations – Andras Vekassy (University of Southampton) Poster 3 - Combining Ultrasonic Methods and Machine Learning Techniques to Assess Baked Products Quality – Erhan Gulsen (University of Nottingham) Poster 4 - Interactive Knowledge-Based Solvent Selection Tool – Hewan Zewdu (University of Nottingham)Poster 5 - CV in High Throughput Chemistry – Jamie Longino (University of Strathclyde)Poster 9 - Dewetting in Thin Liquid Films: Using Sparse Optimization to Learn Evolution Equations – Aspen Fenzl (University of Sheffield)Poster 12 - Creating a merged dataset and its exploration with different Machine Learning algorithms – Maximilian Hoffman (Freie Universität of Berlin)Poster 14 - Bayesian optimisation in Chemistry – Rubaiyat Khondaker (University of Cambridge) Poster 15 - A deep neural network for generation of functional organic materials – Rhyan Barrett (University of Warwick) Thank you to our sponsors Optibrium (https://www.optibrium.com/) and Dotmatics (https://www.dotmatics.com/) who supported this event. These poster presentations were live cartooned by ErrantScience (errantscience.com) which is also available on our YouTube Channel. Sections Intro: (0:00) Andras Vekassy - Nearer the nearsighted principle: Large-scale quantum chemical calculations: (0:17) Erhan Gulsen - Combining Ultrasonic Methods and Machine Learning Techniques to Assess Baked Products Quality: (06:11) Hewan Zewdu - Interactive Knowledge-Based Solvent Selection Tool: (12:09) Jamie Longino - CV in High Throughput Chemistry: (16:52) Aspen Fenzl - Dewetting in Thin Liquid Films: Using Sparse Optimization to Learn Evolution Equations: (21:21) Maximillian Hoffman - Creating a merged dataset and its exploration with different machine learning algorithms: (27:31) Rubaiyat Khondaker - Bayesian optimisation in Chemistry: (34:33) Rhyan Barrett - A deep neural network for generation of functional organic materials: (40:06) Further details from this series can be found at: https://www.ai3sd.org/skills4scientists This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1 and PSDS (Physical Sciences Data science Service) which is funded by EPSRC under Grant Number EP/S020357/1.

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms’ parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol’s capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.

This year 15 interns join us for a 10 week programme between 28th June and 25th September 2021. The projects they are working on are interdisciplinary and include both cutting-edge AI and cutting-edge chemical discovery and demonstrate how and why they are relevant to AI3SD. The projects must be able to demonstrate valuable outputs both with respect to developing student skill and providing impact to AI3SD.The Interns will be required to produce a poster for the AI3SD Summer Project Symposia 1st - 2nd September 2021, and they will take part in our Skills4Scientists programme that will run weekly across July and August whereby the interns will be given training in a range of research, technical and interpersonal skills, alongside informative career-based events.

Innate recognition of viruses is an essential part of the immune response to viral pathogens. This is integral to the maintenance of healthy lungs, which are free from infection and efficient at gaseous exchange. An important component of innate immunity in identifying viruses is the family of C-type collagen containing lectins, also known as collectins. These secreted, soluble proteins are pattern recognition receptors (PRRs) which recognise pathogen-associated molecular patterns (PAMPs), including viral glycoproteins. These innate immune proteins are composed of trimerized units which oligomerise into higher-order structures and facilitate clearance of viral pathogens through multiple mechanisms. Similarly, many viral surface proteins form trimeric configurations, despite not showing primary protein sequence similarities across virus classes and families they belong to. In this review, we discuss the role of lung collectins surfactant proteins A and D (SP-A and SP-D) in viral recognition. Particularly, we focus on the structural similarity and complementarity of these trimeric collectins with the trimeric viral fusion proteins with which, we hypothesise, they have elegantly co-evolved. Recombinant versions of these innate immune proteins may have therapeutic potential in a range of infectious and inflammatory lung diseases including as anti-viral therapeutics.

International audience; Carlo Lamberti opened discussion of the introductory lecture by Bruce Gates: As you have shown, EXAFS plays a crucial role in determining the structure and the nuclearity of nanoparticles (NPs). For each shell, the accuracy of this determination depends on the error bar associated to the coordination number, that strongly correlates with the corresponding Debye–Waller (DW) parameter. This becomes even more important when in situ operando experiments are performed at reaction temperature. Based on your experience, what suggestions can you give to reduce this correlation and increase the potentiality of the technique? Do you believe it is possible to fix or to determine, in a reliable way, DW parameters from independent experimental or computational works? Do you believe that in temperature-dependent experiments it is reliable to adopt th eDebye or the Einstein model to parametrize the evolution of DW parameters?

The solution of the Poisson equation is a crucial step in electronic structure calculations, yielding the electrostatic potential—a key component of the quantum mechanical Hamiltonian. In recent decades, theoretical advances and increases in computer performance have made it possible to simulate the electronic structure of extended systems in complex environments. This requires the solution of more complicated variants of the Poisson equation, featuring nonhomogeneous dielectric permittivities, ionic concentrations with nonlinear dependencies, and diverse boundary conditions. The analytic solutions generally used to solve the Poisson equation in vacuum (or with homogeneous permittivity) are not applicable in these circumstances, and numerical methods must be used. In this work, we present DL_MG, a flexible, scalable, and accurate solver library, developed specifically to tackle the challenges of solving the Poisson equation in modern large-scale electronic structure calculations on parallel computers. Our solver is based on the multigrid approach and uses an iterative high-order defect correction method to improve the accuracy of solutions. Using two chemically relevant model systems, we tested the accuracy and computational performance of DL_MG when solving the generalized Poisson and Poisson–Boltzmann equations, demonstrating excellent agreement with analytic solutions and efficient scaling to ∼10^9 unknowns and 100s of CPU cores. We also applied DL_MG in actual large-scale electronic structure calculations, using the ONETEP linear-scaling electronic structure package to study a 2615 atom protein–ligand complex with routinely available computational resources. In these calculations, the overall execution time with DL_MG was not significantly greater than the time required for calculations using a conventional FFT-based solver.

Field-Effect Transistor sensors (FET-sensors) have been receiving increasing attention for biomolecular sensing over the last two decades due to their potential for ultra-high sensitivity sensing, label-free operation, cost reduction and miniaturisation. Whilst the commercial application of FET-sensors in pH sensing has been realised, their commercial application in biomolecular sensing (termed BioFETs) is hindered by poor understanding of how to optimise device design for highly reproducible operation and high sensitivity. In part, these problems stem from the highly interdisciplinary nature of the problems encountered in this field, in which knowledge of biomolecular-binding kinetics, surface chemistry, electrical double layer physics and electrical engineering is required. In this work, a quantitative analysis and critical review has been performed comparing literature FET-sensor data for pH-sensing with data for sensing of biomolecular streptavidin binding to surface-bound biotin systems. The aim is to provide the first systematic, quantitative comparison of BioFET results for a single biomolecular analyte, specifically Streptavidin, which is the most commonly used model protein in biosensing experiments, and often used as an initial proof-of-concept for new biosensor designs. This novel quantitative and comparative analysis of the surface potential behaviour of a range of devices demonstrated a strong contrast between the trends observed in pH-sensing and those in biomolecule-sensing. Potential explanations are discussed in detail and surface-chemistry optimisation is shown to be a vital component in sensitivity-enhancement. Factors which can influence the response, yet which have not always been fully appreciated, are explored and practical suggestions are provided on how to improve experimental design.