Characterization of novel small molecule inhibitors of estrogen receptor-activation function 2 (ER-AF2) DOI Creative Commons

Jane Foo,

Francesco Gentile, Shabnam Massah

и другие.

Breast Cancer Research, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 26, 2024

Abstract Up to 40% of patients with estrogen receptor (ER)-positive breast cancer will develop resistance against the majority current ER-directed therapies. Resistance can arise through various mechanisms such as increased expression levels coregulators, and key mutations acquired in receptor’s ligand binding domain rendering it constitutively active. To overcome these mechanisms, we explored targeting ER Activation Function 2 (AF2) site, which is essential for coactivator activation. Using artificial intelligence deep docking methodology, virtually screened > 1 billion small molecules identified 290 potential AF2 binders that were then characterized validated an iterative screening pipeline cell-based cell-free assays. We ranked compounds based on their ability reduce transcriptional activity viability ER-positive cells. a lead compound, VPC-260724, inhibits at low micromolar range. confirmed its direct ER-AF2 site PGC1α peptide displacement experiment. proximity ligation assays, showed VPC-260724 disrupts interaction between SRC-3 reduces target genes models including tamoxifen resistant cell line TamR3. In conclusion, developed novel binder, shows antiproliferative models. The use inhibitor combination treatments may provide complementary therapeutic approach treatment cancer.

Язык: Английский

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson’s Disease Associated Protein DOI
Fengling Li, Suzanne Ackloo, C.H. Arrowsmith

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер 64(22), С. 8521 - 8536

Опубликована: Ноя. 5, 2024

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field computational hit-finding. Here we report results inaugural challenge which 23 teams each selected up 100 commercially available compounds that they predicted would bind WDR domain Parkinson's disease target LRRK2, with no known ligand and only an apo structure PDB. lack binding data presumably low druggability is hit finding methods. Of 1955 molecules by participants Round 1 challenge, 73 were found LRRK2 SPR assay KD lower than 150 μM. These advanced 2 expansion phase, where 50 analogs. Binding was observed two orthogonal assays for seven chemically diverse series, affinities ranging from 18 140 successful workflows varied their screening strategies techniques. Three used molecular dynamics produce conformational ensemble targeted site, three included fragment docking step, implemented generative design strategy five one or more deep learning steps. #1 reflects highly exploratory phase drug adopted strikingly diverging strategies. Machine learning-accelerated methods achieved similar brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed rare weakly potent, indicating recent advances not sufficient effectively address challenging targets.

Язык: Английский

Процитировано

7
Active Learning to Select the Most Suitable Reagents and One-Step Organic Chemistry Reactions for Prioritizing Target-Specific Hits from Ultralarge Chemical Spaces DOI

V. G. KOZYREV,

François Sindt,

Didier Rognan

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Янв. 16, 2025

Designing chemically novel and synthesizable ligands from the largest possible chemical space is a major issue in modern drug discovery to identify early hits that are easily amenable medicinal chemistry optimization. Starting sole three-dimensional structure of protein binding site, we herewith describe fully automated active learning protocol propose commercial reagents one-step organic reactions necessary enumerate target-specific primary ultralarge spaces. When applied different scenarios (single transform multiple transforms) addressing spaces various sizes (from 670 million 4.5 billion compounds), method was able recover up 98% virtual discovered by an exhaustive docking-based approach while scanning only 5% full space. It therefore applicable structure-based screening trillion-sized at very high throughput with minimal computational resources.

Язык: Английский

Процитировано

0
Discovery of therapeutic promising natural products to target Kv1.3 channel, a transmembrane protein regulating immune disorders, through multidimensional virtual screening, molecular dynamics simulations and biological validation DOI
Shan Zhang, Na Chen, Fei Wu

и другие.

International Journal of Biological Macromolecules, Год журнала: 2025, Номер unknown, С. 142636 - 142636

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era DOI
Jiyan Dai, Ziyi Zhou, Yanru Zhao

и другие.

European Journal of Medicinal Chemistry, Год журнала: 2024, Номер 283, С. 117162 - 117162

Опубликована: Дек. 11, 2024

Язык: Английский

Процитировано

3
Discovery of Crystallizable Organic Semiconductors with Machine Learning DOI Creative Commons
H. M. Johnson, Filipp Gusev, Jordan T. Dull

и другие.

Journal of the American Chemical Society, Год журнала: 2024, Номер 146(31), С. 21583 - 21590

Опубликована: Июль 25, 2024

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules that capability transforming into a platelet morphology feature both high melting point (

Язык: Английский

Процитировано

1
CACHE Challenge #1: Docking with GNINA Is All You Need DOI Creative Commons
Ian Dunn, Somayeh Pirhadi, Yao Wang

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array structure-based methods identify hits target with no known ligands. utilized two methods, pharmacophore search and molecular docking, initial hit list compounds for expansion phase. Unlike many other participants, we limited ourselves using docking scores in identifying ranking hits. Our resulting best series tied place when evaluated by panel expert judges. Here, report top-performing open-source workflow results.

Язык: Английский

Процитировано

1
Discovery of Crystallizable Organic Semiconductors with Machine Learning DOI Creative Commons
H. M. Johnson, Filipp Gusev, Jordan T. Dull

и другие.

Опубликована: Апрель 17, 2024

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules that capability transforming into a platelet morphology feature both high melting point (Tm) driving force (ΔGc). In this work, we employed machine learning (ML) identify candidate materials potential by estimating aforementioned properties. Six identified ML algorithm were experimentally evaluated; three crystallized platelets, one spherulite, two resisted film crystallization. These results demonstrate successful application scope predicting properties reinforce principles Tm ΔGc metrics govern films.

Язык: Английский

Процитировано

0
Discovery of Crystallizable Organic Semiconductors with Machine Learning DOI Creative Commons
H. M. Johnson, Filipp Gusev, Jordan T. Dull

и другие.

Опубликована: Июнь 25, 2024

Crystalline organic semiconductors are known to have improved charge carrier mobility and exciton diffusion length in comparison their amorphous counterparts. Certain molecular thin films can be transitioned from initially prepared layers large-scale crystalline via abrupt thermal annealing. Ideally, these crystallize as platelets with long-range-ordered domains on the scale of tens hundreds microns. However, other may instead spherulites or resist crystallization entirely. Organic molecules that capability transforming into a platelet morphology feature both high melting point (Tm) driving force (ΔGc). In this work, we employed machine learning (ML) identify candidate materials potential by estimating aforementioned properties. Six identified ML algorithm were experimentally evaluated; three crystallized platelets, one spherulite, two resisted film crystallization. These results demonstrate successful application scope predicting properties reinforce principles Tm ΔGc metrics govern films.

Язык: Английский

Процитировано

0
Characterization of novel small molecule inhibitors of estrogen receptor-activation function 2 (ER-AF2) DOI Creative Commons

Jane Foo,

Francesco Gentile, Shabnam Massah

и другие.

Breast Cancer Research, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 26, 2024

Abstract Up to 40% of patients with estrogen receptor (ER)-positive breast cancer will develop resistance against the majority current ER-directed therapies. Resistance can arise through various mechanisms such as increased expression levels coregulators, and key mutations acquired in receptor’s ligand binding domain rendering it constitutively active. To overcome these mechanisms, we explored targeting ER Activation Function 2 (AF2) site, which is essential for coactivator activation. Using artificial intelligence deep docking methodology, virtually screened > 1 billion small molecules identified 290 potential AF2 binders that were then characterized validated an iterative screening pipeline cell-based cell-free assays. We ranked compounds based on their ability reduce transcriptional activity viability ER-positive cells. a lead compound, VPC-260724, inhibits at low micromolar range. confirmed its direct ER-AF2 site PGC1α peptide displacement experiment. proximity ligation assays, showed VPC-260724 disrupts interaction between SRC-3 reduces target genes models including tamoxifen resistant cell line TamR3. In conclusion, developed novel binder, shows antiproliferative models. The use inhibitor combination treatments may provide complementary therapeutic approach treatment cancer.

Язык: Английский

Процитировано

0