Archived Newsletters

UK-QSAR Autumn 2022

The UK-QSAR and Cheminformatics Group

Welcome to the Autumn 2022 UK-QSAR Newsletter!

Again, our next meeting will be a face-to-face event.  The Autumn UKQSAR & Cheminformatics Society Meeting will be held at the Francis Crick Institute, London, on Tuesday 8th November 2022.  The meeting is organised and hosted jointly by MSD and Cresset, and will comprise an eclectic mix of topics.  More details on the meeting are below. As always, registration is free, but attendees will need to register prior to the event. If you can no longer attend, please let us know so we can offer your space to someone else. Please note that there are train strikes on 7th & 9th November, meaning that trains on 8th November are likely to be disrupted.  We’d advise you to build this into your travel plans for the event. Abstracts and references provided by the speakers are provided below. The date and venue for the Spring 2023 Meeting will be communicated 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

Autumn Meeting Information

The meeting will be held on Tuesday 8th November 2022 at The Francis Crick Institute in London. As ever the meeting is free to attend, although delegates will need to register.  For this meeting, jointly hosted by MSD and Cresset (both of whom have bases in the Crick), an eclectic mix of topics will be covered, ranging from ‘Large Chemical Space’ and ‘FMO’ (Fragment Molecular Orbital) approaches through to ‘New Technology’.  Poster abstracts can be submitted during registration. Speakers include Val Gillet from the University of Sheffield, Franca Klingler from MSD, Iva Lukac from Charnwood Molecular, Alexe Haywood from the University of Nottingham, Stefania Monteleone from Evotec, Daniel Mason and Daniel O’Donovan from HealX, Adrian Mulholland from the University of Bristol and Martin Slater from Cresset.  The provisional agenda is:
9:30 Open registration, coffee/tea
10:15 Welcome and opening remarks Steve St-Gallay, Steve Maginn
Session 1 Large Chemical Space Chair: Mark Mackey
10:30 Chemical Space Docking: Novel ROCK1 Kinase Inhibitors found by Large-Scale Structure-Based Virtual Screening Franca Klingler (MSD)
11:00 Navigating synthetically accessible chemical space Val Gillet (University of Sheffield)
11:30 Kernel Methods for Predicting Yields of Chemical Reactions Alexe Haywood (University of Nottingham)
12:00 Lunch & poster session
Session 2 FMO Chair: Steve St-Gallay
1:30 QM-SAR: Quantum Mechanics Structure-Activity Relationship Iva Lukac (Charnwood Molecular)
2:00 Identification of PPI Hotspots and Modulators Using the FMO-PPI Method Stefania Monteleone (Evotec)
2:30 Break
Session 3 New Technology Chair: Iva Lukac
15:00 Born of Mind and Machine: Augmenting the drug repositioning process with omics- and graph based machine learning Daniel Mason and Daniel O’Donovan (HealX)
15:30 Multiscale Simulation for Drug Resistance and Enzyme Design Adrian Mulholland (University of Bristol)
16:00 Advancing molecular modelling projects through outsourcing Martin Slater (Cresset)
16:30 Concluding remarks, poster prize winner announcement and close Mark Mackey, Susan Boyd
For poster presenters Poster boards measure 1600 mm x 1600 mm and can display one A0 poster per side. If you are able to specify poster sizes then 2 A1 portrait posters will fit per side, so you can display four per board.

About the Venue

The Francis Crick Institute is an independent charity, established to be a UK flagship for discovery research in biomedicine.  The Crick is the outcome of a merger between the MRC’s National Institute for Medical Research (NIMR) and CRUK’s London Research Institute (LRI). The new institute was named after the UK scientist Francis Crick in recognition of his contributions to understanding the genetic code, the key to understanding how living things work. Francis Crick Institute's £700m building 'too noisy to concentrate' |  Science | The Guardian The institute houses more than 2,000 staff and students from over 100 research groups who use their wide-ranging knowledge and expertise to work across disciplines and explore biology at all levels, from molecules through cells to entire organisms. It was established by six founding partner organisations: the Medical Research Council (MRC), Cancer Research UK (CRUK), Wellcome, UCL (University College London), Imperial College London and King’s College London and opened in early 2017.

Travel & Logistics

The Francis Crick Institute (1 Midland Road, London NW1 1AT) is a 2 minute walk from London St Pancras International Station, and 3 minutes from London Kings Cross Station, which can be reached by London Underground (Keeping London moving – Transport for London (  There is no parking on-site. Please note the introductory comments on train strikes and plan accordingly!
If you require overnight accommodation, some nearby hotels include: There is no smoking or vaping permitted on-site.

Abstracts & Pre-Reading Material

Some speakers have provided the following abstracts and references which might be of interest ahead of their talks.

Val Gillet, University of Sheffield

Navigating synthetically accessible chemical space

Suggested reading:

  • Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J & Gillet VJ (2021) RENATE : a pseudo-retrosynthetic tool for synthetically accessible de novo design. Molecular Informatics.
  • Ghiandoni G, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J & Gillet V (2020) Enhancing reaction-based de novo design using a multi-label reaction class recommender. Journal of Computer-Aided Molecular Design. 34, pages783–803.
  • Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J & Gillet V (2019) Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature. Journal of Chemical Information and Modeling, 59(10), 4167-4187. https://
  • Patel H, Gillet VJ, Chen B & Bodkin MJ (2009) Knowledge-based approach to de Novo design using reaction vectors. Journal of Chemical Information and Modeling, 49(5), 1163-1184. https://

Franca Klingler, MSD

Chemical Space Docking: Novel ROCK1 Kinase Inhibitors found by Large-Scale Structure-Based Virtual Screening

We present Chemical Space Docking, a novel virtual screening method and its application on ROCK1 kinase.  The approach combines two distinct advances: (1) it avoids full library enumeration, (2) products are evaluated by molecular docking, which leverages protein structural information. To our knowledge, this is the only structure-based virtual screening technique that effectively facilitates mining billions of molecules. We applied Chemical Space Docking to identify inhibitors of ROCK1 kinase from almost one billion commercially available synthesis-on-demand compounds.  From 69 synthesized molecules, 39% had Ki values below 10 µM.  Two leads were crystallized with the ROCK1 protein, and the structures showed excellent agreement with the docking poses. Our approach scales roughly with the number of building blocks that span a chemical space and is therefore multiple orders of magnitude faster than traditional docking of fully enumerated libraries.

Iva Lukac, Charnwood Molecular

QM-SAR: Quantum Mechanics Structure-Activity Relationship

Protein-ligand binding affinities are driven by the balance between multiple factors, many of which can only partially be accounted for by force field-based methods. On the contrary, the first principle nature of quantum mechanics (QM) calculations enables systematic improvements to the accuracy by which biomolecular recognition is described. Despite the greater accuracy, QM methods have not been routinely used in drug discovery due to the high computational costs required to deal with large biological systems. The fragment molecular orbital (FMO) method is an approach in which QM calculations are performed on fragments, thus enabling a high level of accuracy with very high efficiency. Pairwise interaction energy (PIE) between any fragment pairs can then be decomposed into four energy terms: electrostatics, exchange-repulsion, charge transfer, and dispersion, offering an unprecedented insight into the nature of protein-ligand binding. This talk will show examples in which the FMO method was applied to different problems in the early stages of drug discovery, from assessing water energetics, to helping the design of macrocycles, fragment growing, and lead optimisation.

Suggested reading:

Heifetz A, James T, Southey M, Bodkin MJ, Bromidge S. Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method. Methods Mol Biol. 2020;2114:37-48, doi: PMID: 32016885.

Heifetz A, Sladek V, Townsend-Nicholson A, Fedorov DG. Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method. Methods Mol Biol. 2020;2114:187-205, doi: PMID: 32016895.

Lukac, I., Wyatt, P.G., Gilbert, I.H. et al. Ligand binding: evaluating the contribution of the water molecules network using the Fragment Molecular Orbital method. J Comput Aided Mol Des 35, 1025–1036 (2021).

Stefania Monteleone, Evotec

Identification of PPI Hotspots and Modulators Using the FMO-PPI Method

Protein functions and signalling are mediated by protein-protein interactions (PPIs) and the identification of the key interacting residues at the interface (hotspots) is the first step for the design of PPI modulators. A fast and accurate way to obtain a list of interactions between key residues, including their chemical nature (electrostatic or hydrophobic) and strength (in kcal/mol) is the Fragment Molecular Orbital (FMO) method.We combined FMO and PPI exploration in a new workflow (FMO-PPI1) to identify not only the PPI hotspots, but also the intramolecular interactions and significant water bridges that stabilize the interface. We benchmarked FMO-PPI with a dataset of protein-protein complexes that represent different protein subfamilies and compared its outcome to published site directed mutagenesis data. We also showed that FMO-PPI can be used to support structure-based drug design of PPI modulators. Here we will present examples of its application to the hit-to-lead and lead optimisation phases of PPI inhibitors and molecular glues.

Suggested reading:

Daniel Mason and Daniel O’Donovan, HealX

Born of Mind and Machine: Augmenting the drug repositioning process with omics- and graph based machine learning

There are 7,000 known rare diseases that affect 400 million people across the globe, and 95% of them have no approved treatment. Healx are pioneering the next generation of drug discovery by applying machine learning technology to accelerate the pace, increase the scale and improve the chance of success of rare disease treatment development. In this talk Dan O’Donovan and Dan Mason (both early employees and principal engineers / scientists in the research and development team) will discuss how the latest developments in computational chemistry, biology and machine learning research can benefit the lives of millions of patients.

Adrian Mulholland, University of Bristol

Multiscale Simulation for Drug Resistance and Enzyme Design

Molecular simulations are revealing mechanisms of drug resistance and allosteric effects in proteins, including the SARS-CoV-2 main protease and spike protein, and beta-lactamase enzymes that break down beta-lactam antibiotics. Dynamical-nonequilibrium molecular dynamics (D-NEMD) simulations are an emerging approach to identify allosteric communication pathways and distal positions associated with drug resistance mutations. Combined quantum mechanics/molecular mechanics (QM/MM) simulations reveal mechanisms of chemical reactions in proteins. They distinguish enzymes capable of breaking down specific antibiotics, and identify physical features that determine catalytic activity. Simulations can be used as ‘computational assays’ to predict functionally relevant properties, and contribute to de novo enzyme design.

For allosteric effects from D-NEMD simulations:

Oliveira, A.S.F., Ciccotti, G., Haider, S., Mulholland AJ. Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems. Eur. Phys. J. B 94, 144 (2021),

For enzyme design and evolution:

Adrian Bunzel, J.L. Ross Anderson, Adrian J. Mulholland, Designing better enzymes: Insights from directed evolution, Current Opinion in Structural Biology, 67, 2021, 212-218,

For multiscale methods:

Amaro, R., Mulholland, A. Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nat Rev Chem 2, 0148 (2018),,

For interactive VR in molecular design:

Rebecca K. Walters, Ella M. Gale, Jonathan Barnoud, David R. Glowacki & Adrian J. Mulholland (2022) The emerging potential of interactive virtual reality in drug discovery, Expert Opinion on Drug Discovery, 17:7, 685-698, DOI: 10.1080/17460441.2022.2079632

Alexe Haywood, University of Nottingham

Kernel Methods for Predicting Yields of Chemical Reactions

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions (see below). The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction component. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.T. Ahneman, J. G. Estrada, S. Lin, S. D. Dreher, A. G. Doyle, Science, 2018, 360, 186-190Alexe L. Haywood, Joseph Redshaw, Magnus W. D. Hanson-Heine, Adam Taylor, Alex Brown, Andrew M. Mason, Thomas Gärtner, and Jonathan D. Hirst, J. Chem. Inf. Model., 2022, 62, 9, 2077–2092.

Martin Slater, Cresset

Advancing molecular modelling projects through outsourcing



Upcoming Meetings

The following meetings may be of interest to our readers:

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