MétaCan
Menu
Back to cohort

Efficient large-scale exploration of fragment hit progression by exploiting binding-site purification of actives (B-SPA) through combining multi-step array synthesis and HT crystallography.

2024· preprint· en· W4391692003 on OpenAlexafffund
Harold Grosjean, A. Aimon, Storm Hassell‐Hart, Warren Thompson, L. Koekemoer, James M. Bennett, C.A. Anderson, Conor Wild, W.J. Bradshaw, Edward A. Fitzgerald, T. Krojer, A.R. Bradley, Oleg Fedorov, Philip C. Biggin, John Spencer, F. von Delft

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsDiscovery Centre
FundersEngineering and Physical Sciences Research CouncilFundação de Apoio à Pesquisa do Distrito FederalConselho Nacional de Desenvolvimento Científico e TecnológicoOntario Ministry of Economic Development and InnovationMinistero dello Sviluppo EconomicoInternational Seafood Sustainability FoundationEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAGenome CanadaFundação de Amparo à Pesquisa do Estado de São PauloDiamond Light SourceNovartis PharmaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorPfizer
KeywordsFragment (logic)ChemistryScale (ratio)Combinatorial chemistryComputational biologyComputer scienceAlgorithmBiologyPhysics

Abstract

fetched live from OpenAlex

Fragment approaches are long-established in target-based ligand discovery. Nevertheless, their full transformative potential lies dormant, because progressing hits to potency remains difficult and underserved by methodology developments, which mostly focus on screening. The only credible progression paradigm is conventional design-make-test analyse (DMTA) medicinal chemistry, which is costly and thus necessitates picking winners early, thereby effectively discarding all the other hits. We here demonstrate the workability of an alternative strategy, namely immediate large-scale exploration of diverse hit-inspired compounds. The key insight is that 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, namely crystallography of fragment screening. This has the sensitivity to detect even 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 second bromodomain of human PHIP, 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 were entirely driven by synthetic tractability. Expected product was present in 1108 CRMs, as detected by automated mass spectrometry; and 22 individual products were resolved in crystal structures of CRMs added to crystals. These provided an initial SAR map, revealed pose stability in 19 and instability in 3 products, and resolved stereochemical preference. Unexpectedly, in view of the naïve design approach, one resolved compound even showed on-scale biochemical potency (IC50=34 μM) and biophysical affinity (Kd=50 μM) after resynthesis. This binding-site purification of actives (B-SPA) process is formulaic and engineerable, here yielding the output of >25 person-years in ~20 days, with solvent use reduced from >4,500L to <20L. Thus, this approach, coupled with algorithmically guided compound and reaction design and new formalisms for data analysis, lends itself to routine fragment progression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.276
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueChemRxivSame topicChemical Synthesis and AnalysisFrench-language works237,207