I03-Macromolecular Crystallography
I04-1-Macromolecular Crystallography (fixed wavelength)
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Harold
Grosjean
,
Anthony
Aimon
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Storm
Hassell-Hart
,
Warren
Thompson
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Lizbe
Koekemoer
,
James
Bennett
,
Anthony
Bradley
,
Cameron
Anderson
,
Conor
Wild
,
William J.
Bradshaw
,
Edward A.
Fitzgerald
,
Tobias
Krojer
,
Oleg
Fedorov
,
Philip C.
Biggin
,
John
Spencer
,
Frank
Von Delft
Diamond Proposal Number(s):
[19301]
Abstract: Fragment approaches are long-established in target-based ligand discovery yet their full transformative potential lies dormant, because progressing hits to potency remains underserved by methodological work. The only credible progression paradigm is multiple cycles of costly conventional design-make-test-analyse (DMTA) medicinal chemistry, necessitating picking winners early and discarding others. It is effective to cheaply parallelize large numbers of non-uniform multi-step reactions, because, even without compound purification, a high-quality readout of binding is available, viz. crystallography. This can detect low-level binding of slightly active compounds, which the targeted binding site extracts directly from crude reaction mixtures (CRMs). In this proof-of-concept study, we expand a fragment hit from a crystal-based screen of the bromodomain PHIP2, using array synthesis on low-cost robotics to implement 6 independent multi-step reaction routes of up to 5 steps, attempting the synthesis of 1876 diverse expansions, designs entirely driven by synthetic tractability. The expected product was present in 1108 (59%) CRMs, detected by automated mass spectrometry, 22 individual products were resolved in crystal structures of CRMs added to crystals, providing an initial SAR map, pose stability in 19 and instability in 3 products and resolved stereochemical preference. One compound showed biochemical potency (IC50=34 μM) and affinity (Kd=50 μM) after resynthesis.
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Feb 2025
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Open Access
Abstract: Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show that an algorithmic approach (Fragmenstein) that ‘stitches’ the ligand atoms from this structural information together can provide more accurate and reliable predictions for protein–ligand complex conformation than general methods such as pharmacophore-constrained docking. This approach works under the assumption of conserved binding: when a larger molecule is designed containing the initial fragment hit, the common substructure between the two will adopt the same binding mode. Fragmenstein either takes the atomic coordinates of ligands from a experimental fragment screen and combines the atoms together to produce a novel merged virtual compound, or uses them to predict the bound complex for a provided molecule. The molecule is then energy minimised under strong constraints to obtain a structurally plausible conformer. The code is available at https://github.com/oxpig/Fragmenstein.
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Jan 2025
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Daren
Fearon
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Ailsa
Powell
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Alice
Douangamath
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Alexandre
Dias
,
Charles W. E.
Tomlinson
,
Blake H.
Balcomb
,
Jasmin C.
Aschenbrenner
,
Anthony
Aimon
,
Isabel A.
Barker
,
Jose
Brandao-Neto
,
Patrick
Collins
,
Louise E.
Dunnett
,
Michael
Fairhead
,
Richard J.
Gildea
,
Mathew
Golding
,
Tyler
Gorrie-Stone
,
Paul V.
Hathaway
,
Lizbe
Koekemoer
,
Tobias
Krojer
,
Ryan
Lithgo
,
Elizabeth M.
Maclean
,
Peter G.
Marples
,
Xiaomin
Ni
,
Rachael
Skyner
,
Romain
Talon
,
Warren
Thompson
,
Conor F.
Wild
,
Max
Winokan
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Nathan D.
Wright
,
Graeme
Winter
,
Elizabeth J.
Shotton
,
Frank
Von Delft
Open Access
Abstract: Fragment-based drug discovery is a well-established method for the identification of chemical starting points for development into clinical candidates. Historically, crystallographic fragment screening was perceived to be low-throughput and time consuming. However, thanks to advances in synchrotron capabilities and the introduction of dedicated facilities, such as the XChem platform at Diamond Light Source, there have been substantial improvements in throughput and integration between sample preparation, data collection and hit identification. Herein we share our experiences of establishing a crystallographic fragment screening facility, our learnings from operating a user programme for ten years and our perspective on applying structural enablement to rapidly progress initial fragment hits to lead-like molecules.
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Nov 2024
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Claudia
Tredup
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Suzanne
Ackloo
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Hartmut
Beck
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Peter J.
Brown
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Alex N.
Bullock
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Alessio
Ciulli
,
Ivan
Dikic
,
Kristina
Edfeldt
,
Aled M.
Edwards
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Jonathan M.
Elkins
,
Henner F.
Farin
,
Edward A.
Fon
,
Matthias
Gstaiger
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Judith
Günther
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Anna-Lena
Gustavsson
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Sandra
Häberle
,
Laura
Isigkeit
,
Kilian V. M.
Huber
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Andras
Kotschy
,
Oliver
Krämer
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Andrew R.
Leach
,
Brian D.
Marsden
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Hisanori
Matsui
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Daniel
Merk
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Florian
Montel
,
Monique P. C.
Mulder
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Susanne
Müller
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Dafydd R.
Owen
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Ewgenij
Proschak
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Sandra
Röhm
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Alexandra
Stolz
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Michael
Sundström
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Frank
Von Delft
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Timothy M.
Willson
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Cheryl H.
Arrowsmith
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Stefan
Knapp
Open Access
Abstract: Target 2035 is a global initiative that seeks to identify a pharmacological modulator of most human proteins by the year 2035. As part of an ongoing series of annual updates of this initiative, we summarise here the efforts of the EUbOPEN project whose objectives and results are making a strong contribution to the goals of Target 2035. EUbOPEN is a public–private partnership with four pillars of activity: (1) chemogenomic library collections, (2) chemical probe discovery and technology development for hit-to-lead chemistry, (3) profiling of bioactive compounds in patient-derived disease assays, and (4) collection, storage and dissemination of project-wide data and reagents. The substantial outputs of this programme include a chemogenomic compound library covering one third of the druggable proteome, as well as 100 chemical probes, both profiled in patient derived assays, as well as hundreds of data sets deposited in existing public data repositories and a project-specific data resource for exploring EUbOPEN outputs.
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Nov 2024
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Martin P.
Schwalm
,
Johannes
Dopfer
,
Adarsh
Kumar
,
Francesco A.
Greco
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Nicolas
Bauer
,
Frank
Löhr
,
Jan
Heering
,
Sara
Cano-Franco
,
Severin
Lechner
,
Thomas
Hanke
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Ivana
Jaser
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Viktoria
Morasch
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Christopher
Lenz
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Daren
Fearon
,
Peter G.
Marples
,
Charles W. E.
Tomlinson
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Lorene
Brunello
,
Krishna
Saxena
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Nathan B. P.
Adams
,
Frank
Von Delft
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Susanne
Müller
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Alexandra
Stolz
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Ewgenij
Proschak
,
Bernhard
Kuster
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Stefan
Knapp
,
Vladimir V.
Rogov
Diamond Proposal Number(s):
[29658]
Open Access
Abstract: Recent successes in developing small molecule degraders that act through the ubiquitin system have spurred efforts to extend this technology to other mechanisms, including the autophagosomal-lysosomal pathway. Therefore, reports of autophagosome tethering compounds (ATTECs) have received considerable attention from the drug development community. ATTECs are based on the recruitment of targets to LC3/GABARAP, a family of ubiquitin-like proteins that presumably bind to the autophagosome membrane and tether cargo-loaded autophagy receptors into the autophagosome. In this work, we rigorously tested the target engagement of the reported ATTECs to validate the existing LC3/GABARAP ligands. Surprisingly, we were unable to detect interaction with their designated target LC3 using a diversity of biophysical methods. Intrigued by the idea of developing ATTECs, we evaluated the ligandability of LC3/GABARAP by in silico docking and large-scale crystallographic fragment screening. Data based on approximately 1000 crystal structures revealed that most fragments bound to the HP2 but not to the HP1 pocket within the LIR docking site, suggesting a favorable ligandability of HP2. Through this study, we identified diverse validated LC3/GABARAP ligands and fragments as starting points for chemical probe and ATTEC development.
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Nov 2024
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Thibault
Vantieghem
,
Nayyar A.
Aslam
,
Evgenii M.
Osipov
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Muluembet
Akele
,
Siska
Van Belle
,
Steven
Beelen
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Matúš
Drexler
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Terezia
Paulovcakova
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Vanda
Lux
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Daren
Fearon
,
Alice
Douangamath
,
Frank
Von Delft
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Frauke
Christ
,
Václav
Veverka
,
Peter
Verwilst
,
Arthur
Van Aerschot
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Zeger
Debyser
,
Sergei V.
Strelkov
Diamond Proposal Number(s):
[25544]
Abstract: Lens epithelium-derived growth factor p75 (LEDGF/p75), member of the hepatoma-derived growth-factor-related protein (HRP) family, is a transcriptional co-activator and involved in several pathologies including HIV infection and malignancies such as MLL-rearranged leukemia. LEDGF/p75 acts by tethering proteins to the chromatin through its integrase binding domain. This chromatin interaction occurs between the PWWP domain of LEDGF/p75 and nucleosomes carrying a di- or trimethylation mark on histone H3 Lys36 (H3K36me2/3). Our aim is to rationally devise small molecule drugs capable of inhibiting such interaction. To bootstrap this development, we resorted to X-ray crystallography-based fragment screening (FBS-X). Given that the LEDGF PWWP domain crystals were not suitable for FBS-X, we employed crystals of the closely related PWWP domain of paralog HRP-2. As a result, as many as 68 diverse fragment hits were identified, providing a detailed sampling of the H3K36me2/3 pocket pharmacophore. Subsequent structure-guided fragment expansion in three directions yielded multiple compound series binding to the pocket, as verified through X-ray crystallography, nuclear magnetic resonance and differential scanning fluorimetry. Our best compounds have double-digit micromolar affinity and optimally sample the interactions available in the pocket, judging by the Kd-based ligand efficiency exceeding 0.5 kcal/mol per non-hydrogen atom. Beyond π-stacking within the aromatic cage of the pocket and hydrogen bonding, the best compounds engage in a σ-hole interaction between a halogen atom and a conserved water buried deep in the pocket. Notably, the binding pocket in LEDGF PWWP is considerably smaller compared to the related PWWP1 domains of NSD2 and NSD3 which feature an additional subpocket and for which nanomolar affinity compounds have been developed recently. The absence of this subpocket in LEDGF PWWP limits the attainable affinity. Additionally, these structural differences in the H3K36me2/3 pocket across the PWWP domain family translate into a distinct selectivity of the compounds we developed. Our top ranked compounds are interacting with both homologous LEDGF and HRP-2 PWWP domains, yet they showed no affinity for the NSD2 PWWP1 and BRPF2 PWWP domains which belong to other PWWP domain subfamilies. Nevertheless, our developed compound series provide a strong foundation for future drug discovery targeting the LEDGF PWWP domain as they can further be explored through combinatorial chemistry. Given that the affinity of H3K36me2/3 nucleosomes to LEDGF/p75 is driven by interactions within the pocket as well as with the DNA-binding residues, we suggest that future compound development should target the latter region as well. Beyond drug discovery, our compounds can be employed to devise tool compounds to investigate the mechanism of LEDGF/p75 in epigenetic regulation.
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Oct 2024
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Open Access
Abstract: Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.
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Mar 2024
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Lauro Ribeiro
De Souza Neto
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Bogar Omar
Montoya
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Jose
Brandao-Neto
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Anil
Verma
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Sebastian
Bowyer
,
José Teófilo
Moreira-Filho
,
Rafael Ferreira
Dantas
,
Bruno Junior
Neves
,
Carolina Horta
Andrade
,
Frank
Von Delft
,
Raymond J.
Owens
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Nicholas
Furnham
,
Floriano Paes
Silva-Jr
Open Access
Abstract: Schistosomiasis is caused by parasites of the genus Schistosoma, which infect more than 200 million people. Praziquantel (PZQ) has been the main drug for controlling schistosomiasis for over four decades, but despite that it is ineffective against juvenile worms and size and taste issues with its pharmaceutical forms impose challenges for treating school-aged children. It is also important to note that PZQ resistant strains can be generated in laboratory conditions and observed in the field, hence its extensive use in mass drug administration programs raises concerns about resistance, highlighting the need to search for new schistosomicidal drugs. Schistosomes survival relies on the redox enzyme thioredoxin glutathione reductase (TGR), a validated target for the development of new anti-schistosomal drugs. Here we report a high-throughput fragment screening campaign of 768 compounds against S. mansoni TGR (SmTGR) using X-ray crystallography. We observed 49 binding events involving 35 distinct molecular fragments which were found to be distributed across 16 binding sites. Most sites are described for the first time within SmTGR, a noteworthy exception being the “doorstop pocket” near the NADPH binding site. We have compared results from hotspots and pocket druggability analysis of SmTGR with the experimental binding sites found in this work, with our results indicating only limited coincidence between experimental and computational results. Finally, we discuss that binding sites at the doorstop/NADPH binding site and in the SmTGR dimer interface, should be prioritized for developing SmTGR inhibitors as new antischistosomal drugs.
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Jan 2024
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I04-1-Macromolecular Crystallography (fixed wavelength)
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Melissa L.
Boby
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Daren
Fearon
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Matteo
Ferla
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Mihajlo
Filep
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Lizbe
Koekemoer
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Matthew C.
Robinson
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The Covid
Moonshot Consortium
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John D.
Chodera
,
Alpha A.
Lee
,
Nir
London
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Annette
Von Delft
,
Frank
Von Delft
Abstract: We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery.
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Nov 2023
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Jack
Scantlebury
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Lucy
Vost
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Anna
Carbery
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Thomas E.
Hadfield
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Oliver M.
Turnbull
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Nathan
Brown
,
Vijil
Chenthamarakshan
,
Payel
Das
,
Harold
Grosjean
,
Frank
Von Delft
,
Charlotte M.
Deane
Open Access
Abstract: Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.
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May 2023
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