| UK-QSAR Spring 2022 |
| The UK-QSAR and Cheminformatics Group |
| Welcome to the Spring 2022 UK-QSAR Newsletter! For the first time in two years, our next meeting is planned to be a face-to-face event. The pandemic may not be over, but with many having already returned to the office/lab, the take-up of vaccines, the immunity acquired from previous infection, and the recent availability of therapeutic antibody treatments and anti-virals, the time seems right to try out an in-person meeting in what could be the “new normal” environment we’ll all need to get used to. As such, we would like to encourage attendees to take a lateral flow test on the morning of the meeting, and (obviously!) only to attend if that is negative. LFTs continue to be freely available from UK pharmacies until the end of March. Mask wearing will, of course, be optional, but is encouraged.The meeting will be on Tuesday 26th April 2022 at 9am, and will be held at Downing College in Cambridge. The event will be hosted by OpenEye Scientific and is themed around Free Energy Calculations in Drug Discovery. More details on the meeting and the venue are below. Registration is free, but attendees will need to register prior to the event. Abstracts and references provided by the speakers are provided below. One possible effect of the pandemic has been an upsurge of interest – and indeed investment – in our sector. New start-ups abound, and the job market for computational & medicinal chemists seems more buoyant than ever. We look at some of the reasons for the uptick in the biotech sector and whether it’s here to stay below. The format for the Autumn 2022 Meeting is still under discussion and will be reviewed in due course. You’ll also find the regular articles on Jobs and Upcoming Meetings. As ever, please send any feedback or suggestions you have for future newsletters to Susan Boyd at newsletter@ukqsar.org. Spring Meeting Information The meeting will be held on Tuesday 26th April 2022 at Downing College, Cambridge. As ever the meeting is free to attend, although delegates will need to register before 25th April. For this meeting, hosted by OpenEye Scientific, we will explore various aspects of binding free energy calculations, with topics covered including new methods for calculating such energies, through to the impact of FEP in design of large virtual libraries. Poster abstracts can be submitted during registration. Speakers include Martin Packer of AstraZeneca, Jonathan Essex from the University of Southampton, David Hahn from Janssen, Francois Berenger from the University of Tokyo, Peter Coveney from the UCL, Hannah Bruce MacDonald from Merck, Bert de Groot from MPI Grottingen and several speakers from OpenEye. The provisional agenda is: 9:00: Open registration, coffee/tea 9:45: Welcome and Opening Remarks, Geoff Skillman, CSO, OpenEye Scientific Session 1 — Chair: Christopher Bayly, OpenEye Scientific 10:00: Jonathan Essex, University of Southampton – Is the sampling of water in protein-ligand systems a solved problem? 10:30: David Hahn, Janssen – Large Scale Free Energy Calculations in Drug Discovery 11:00 – 11:30: Break Session 2 — Chair: Gunther Stahl, OpenEye Scientific 11:30: Francois Berenger, University of Tokyo – Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking 12:00: Christopher Bayly, OpenEye Scientific – Binding Free Energies in Orion: a Parallel Universe 12:30 – 14:00: Lunch Session 3 — Chair: TBA 14:00: Bert de Groot, MPI Goettingen – High throughput relative and absolute non-equilibrium binding free energies with pmx 14:30: Hannah Bruce Macdonald, MSD – Application of free energy methods for lead optimisation 15:00 – 15:30: Break Session 4 — Chair: Christopher Bayly, OpenEye Scientific 15:30: Peter Coveney, UCL – Assembling an arsenal to achieve reliable free energy calculations 16:00: Martin Packer, AstraZeneca – Impact of FEP in prospective molecular design – moving from single edges to large virtual libraries 16:30: Geoff Skillman, OpenEye Scientific – Concluding remarks, poster winner announcement and close. About the Venue Downing College is located in the centre of Cambridge and Cambridge Railway Station is just 15 minutes’ walk away. If you are travelling by car, Cambridge park and ride has free parking, and buses to the city centre cost three pounds for a return ticket – see http://cambridgeparkandride.info/index.shtml for more details. Cambridge Bus Station is 10 minutes’ walk from Downing College. If you require overnight accommodation please have a look at https://www.visitcambridge.org/accommodation for details of hotels in the city centre, or https://www.universityrooms.com/en-GB/city/Cambridge if you’d consider staying in nearby college accommodation. Downing College was founded in 1800 by a bequest from Sir George Downing, using inherited wealth from his grandfather, who not only built 10 Downing Street, but also served under both Cromwell & Charles II. Sir George had no direct heirs (since his child bride refused to live with him, instead opting to serve as a Maid of Honour to Queen Anne) and left his estate to his cousin, Jacob. After much legal dispute, eventually the family allowed the bequest to be used to set up the college, and it was granted a Royal Charter in 1800. It is often described as the oldest of the new colleges and the newest of the old. Downing College was formed “for the encouragement of the study of Law and Medicine and of the cognate subjects of Moral and Natural Science”, and has developed a reputation amongst Cambridge colleges for Law and Medicine.Downing has been named one of the two most eco-friendly colleges in Cambridge.  Now the college sits within an impressive 20 acres of gardens & grounds, right in the centre of Cambridge. The Post-Pandemic Biotech Surge Susan Boyd, CompChem Solutions Ltd. Anyone working in computational chemistry, chemoinformatics or medicinal chemistry in the last few months can hardly have failed to notice the buoyancy of the job market. Our scientists and business professionals are in high demand, often being targeted directly by recruitment consultants seeking to catapult them from their current role into some exciting new start-up or newly growing organisation. But what underlies this uptick in investment and jobs in our sector? And will it last?Undoubtedly, the pandemic has raised the profile of biotech, and investors appear more likely to try a punt on a new biotech venture, hoping to be the backer of the next big money-spinning technology for healthcare. The UK’s record in sequencing the Covid variants, developing diagnostics, vaccines and treatments for Covid has attracted not only investment from venture capital, but in 2021 the scale and number of IPOs for UK biotechs increased by 434% compared to the previous year (ref). But new technologies (eg AI, new target-finding platforms, immunotherapies) have also played their part, as has the creation of government-funded programmes to help bridge the gap between business and research (eg the Catapult Network). Whilst the UK leads Europe in terms of new biotech start-ups and funding for those companies (ref) it still lags significantly behind the USA and China for both early stage funding and in particular, translation of new science into commercialised products.  UK companies tend to be less successful at raising capital for late-stage funding than their European counterparts. As such, many industry leaders believe that access to US investors is essential for the success of UK biotechs, since many UK start-ups may end up seeking an IPO on a US exchange to access later-stage finance.If this current biotech bubble is to last, we will need our new technologies to deliver, we will need to attract, retain and coach our life science talent for both early stage and late stage business needs, we will need to foster good relations with US markets, and leverage our partnerships with the best UK life sciences resources available (eg NHS data, UK Biobank, and Innovate UK funded programmes). The near-future looks bright for biotech in the UK. Beyond that, the outlook is less predictable. Abstracts & Pre-Reading Material Some speakers have provided the following abstracts and references which might be of interest ahead of their talks. Francois Berenger, University of Tokyo Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method “lean-docking”. To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.https://www.researchgate.net/publication/350940167_Lean-Docking_Exploiting_Ligands’_Predicted_Docking_Scores_to_Accelerate_Molecular_Docking Bert de Groot, MPI Goettingen High throughput relative and absolute non-equilibrium binding free energies with pmx Alchemical free energy calculations have come of age. Based on rigorous first principles of statistical mechanics, these calculations explore physical paths not experimentally accessible and provide unprecedented accuracy in the prediction of processes as diverse as protein thermostability and ligand binding free energies. Based on the pmx framework coupled to the GROMACS molecular dynamics engine, results of high-throughput relative as well as absolute ligand binding free energies are presented.Suggested reading: https://doi.org/10.1039/C9SC03754C https://doi.org/10.1039/D1SC03472C https://doi.org/10.1021/acs.jcim.1c01445 https://doi.org/10.48550/arXiv.2201.06372 Peter Coveney, UCL Assembling an arsenal to achieve reliable free energy calculations The ability of rapidly and accurately predicting binding affinities of ligands to a target protein of interest would greatly facilitate drug discovery programs by enabling researchers to focus on the compounds with a high probability of being pharmacologically active. Both the machine learning (ML) and physics-based (PB) methods have been increasingly used for the free energy predictions in drug development projects. The methods individually have their own advantages and limitations, which fortunately complement each other. We have coupled the ML and PB into a coherent scientific workflow, bringing together several methods of which some have already been applied in drug discovery while others are relatively new to the field and yet to be adopted. Such a coupled approach creates synergies between PB and ML methods and can significantly improve the outcomes, in terms of both the accuracy of the predictions and the coverage of chemical space. The workflow can be applied to the entire process of early drug discovery stage which involves hit discovery, hit to lead, lead optimization, and evaluation of potential side effects and toxicities. A very large number of compounds can be generated and evaluated, which narrow down to performing several independent calculations concurrently at large scale to increase the throughput. The ensemble computing pattern is ideal for such scenarios, which employs a high throughput “embarrassingly” parallel workload. This workflow is a suite of applications that collectively are able to scale up to exascale machines. We have demonstrated that the innovative, iterative and interactive heterogeneous workflow has the potential to accelerate the existing drug discovery process. Pre-reading references: A. Al Saadi, et al., “IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads,” in 50th International Conference on Parallel Processing, Aug. 2021, Article No.: 40, pp. 1–12. DOI: 10.1145/3472456.3473524.A. P. Bhati, et al., “Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers”, Interface Focus, 11, 20210018, DOI: 10.1098/rsfs.2021.0018S. Wan, A. P. Bhati, S. J. Zasada and P. V. Coveney, “Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction”, Interface Focus 10, 2020007 (2020), DOI:10.1098/rsfs.2020.0007 Martin Packer, AstraZeneca Impact of FEP in prospective molecular design – moving from single edges to large virtual libraries Free energy perturbation (FEP) models provide precise and accurate predictions for protein-ligand binding affinity. Currently available GPU hardware enables us to generate data for a single ligand with compute times of a few hours. Given the potential accuracy of FEP models, it is very desirable to apply them to every ligand that we design, but compute time then becomes a severely limiting factor. Active learning FEP combines FEP data with machine learning algorithms, to generate FEP-based structure activity models using rationally selected subsets from a virtual library. We iterate between FEP and machine learning models until we judge that simple models are predictive for FEP, so that we can spare further detailed computation. In 2020 we applied this approach to a set of 16 active drug design projects within AstraZeneca. We used three approaches to design large virtual libraries and saw positive impact across a diverse set of protein targets. Over the course of 9 months we were able to generate 165,000 FEP data points and used those to prioritise synthesis of 445 molecules. We also used the models to generate detailed SAR maps for new hit series, which we exemplify here using a previously published series for the kinase EphB4. Active learning FEP takes us a step closer towards a design environment in which every virtual molecule is assessed on its predicted affinity and its ADME properties, to focus synthesis and test activities on molecules most likely to meet multiple endpoints required for successful drug design. Suggested reading: https://doi.org/10.1021/acs.jcim.9b00367 Jobs Director, Computational Chemistry, AstraZeneca, Cambridge, UK Director, Computational Chemistry, Data & Computational Sciences, Oxford, UK Director/Head of CADD, Datatronics, Basel, Switzerland Chair in Digital Chemistry, University of Bristol, UK Post-Doctoral Research Associate in ML, University of Cambridge and GSK, UK Associate/Senior Director CADD, Ridgeline Discovery, Basel, Switzerland Head of Molecular Design, Bayer, Wuppertal, Germany Senior Chemoinformatician, Healx, Cambridge, UK Computational Chemist, AstraZeneca, Oss, Netherlands Computational Chemist, Evotec, Abingdon, UK Senior Computational Chemist, Sygnature Discovery, Nottingham or Alderley Park, UK Senior Scientist – CADD, Roche, Basel, Switzerland Multiple positions, Exscientia, Oxford, UK Application Scientist (and other roles), Chemical Computing Group, Cambridge, UK Head of Computational Chemistry, Domainex, Cambridge, UK Senior Scientist CADD, Charles River Laboratories, Cambridge, UK Research Leader CADD, Charles River Laboratories, Cambridge, UK Discovery Science Team Leader, CCDC, Cambridge, UK Computational Chemistry Drug Discovery Scientist, LifeArc, Stevenage, UK Computational Toxicologist, Sanofi, Frankfurt, Germany Scientist, Computational Chemistry, Lundbeck, Copenhagen, Denmark Scientist/Senior Scientist – Computational Chemistry, Dewpoint Therapeutics, Frankfurt, Germany Various positions, Cresset, Litlington, UK Various positions, Healx, Cambridge/Remote, UK Various positions, Benevolent.ai, London, UK Principal Scientist, Computational Chemistry, Turbine AI, Budapest Upcoming Meetings The following meetings may be of interest to our readers: CCG UGM & Conference (Europe), 17-20th May 2022, Amsterdam Cambridge Cheminformatics Meeting, 1st June 2022 Andy Vinter Memorial Meeting, 16th June 2022, Cambridge UK & online Milner Therapeutics Symposium, 28th June 2022, Cambridge UK & online 5th Artificial Intelligence in Chemistry Symposium, 1-2nd September 2022, Cambridge UK & online EuroQSAR, 26-30th September 2022, Heidelberg UKQSAR Autumn 2022 Meeting, Details TBC |