Histone deacetylase 1 (HDAC1, GON-10, HD1, RPD3, RPD3L1, KDAC1) plays a key role in the regulation of eukaryotic gene expression. Numerous reports revealed that HDAC1 is considered one of the most promising targets for cancer therapy.

The mechanistic target of rapamycin (mTOR, mammalian target of rapamycin, FK506-binding protein 12-rapamycin-associated protein 1, FRAP, FRAP1, FRAP2, RAFT1, RAPT1, SKS) is a serine/threonine protein kinase which regulates various cellular processes – cell growth, cell proliferation, survival, protein synthesis, autophagy, transcription, and more. mTOR is frequently overactivated in numerous cancers.

 

The OTAVA Dual HDAC1/mTOR Inhibitors Library consists of 283 compounds, selected using an XGBoost machine learning model for predicted dual activity toward histone deacetylase 1 (HDAC1) and the mechanistic target of rapamycin (mTOR). The library was developed using state-of-the-art cheminformatics approaches, structural filtering, Tanimoto clustering, and PCA analysis, resulting in a structurally and physicochemically diverse collection.

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Target Relevance: HDAC1 and mTOR

• HDAC1 regulates gene expression via histone deacetylation. Its inhibition can activate tumor suppressor genes and trigger apoptosis in cancer cells.

• mTOR regulates cellular growth, metabolism, and angiogenesis. mTOR inhibitors are approved for cancer, autoimmune diseases, and post-transplant therapy.

• Dual inhibition of HDAC1 and mTOR shows synergistic anticancer activity and the ability to overcome drug resistance. [1]

Library Preparation

1. Dataset preparation from ChEMBL

• Activity data retrieved for HDAC1 and mTOR; duplicates and inconsistent structures removed.

• Class labeling: active = pChEMBL > 5 (equivalent to IC₅₀/Ki < 10 µM), inactive = pChEMBL ≤ 5

2. Feature generation

• Morgan fingerprints (radius = 2, 1024 bits)

• Physicochemical descriptors: MolWt, LogP, TPSA, HBA, HBD

3. XGBoost model training

• HDAC1: Accuracy = 0.92, ROC AUC = 0.97

• mTOR: Accuracy = 0.96, ROC AUC = 0.99

4. Screening of OTAVA stock compounds

• Models applied to OTAVA’s internal compound collection

• 283 dual-predicted compounds selected with high probability of activity toward both targets

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Novelty and Diversity Assessment

1. Tanimoto Similarity

• Compared 283 selected compounds against 1564 known HDAC1/mTOR inhibitors

• Distribution of maximum similarity:

• 31 compounds had Tanimoto similarity < 0.25 – indicating novel chemotypes

2. Butina Clustering

• Tanimoto distance cutoff: 0.6

• 96 clusters identified:

  • 50 clusters were singletons

  • 1 major cluster contained 39 compounds

This result highlights a high level of structural diversity within the library. The presence of numerous singleton clusters indicates unique chemotypes, while the existence of one larger cluster may point to a promising core scaffold suitable for further SAR exploration or hit expansion.

3. PCA of Physicochemical Space

• Descriptors: MolWt, LogP, TPSA, HBA, HBD

• PC1 + PC2 explain over 77% of variance

This demonstrates that the physicochemical space of the library is well-distributed and non-redundant. The compounds occupy diverse regions of drug-like chemical space, which increases the probability of finding hits with favorable ADME/Tox properties and target specificity.

4. PCA colored by Tanimoto clusters

5. PCA highlighting structural outliers (Tanimoto < 0.25)

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Applications of the Library

• High-throughput screening for dual HDAC1/mTOR inhibitors

• Hit identification and lead optimization

• SAR exploration for dual-target scaffolds

• Evaluation of synergistic inhibition strategies

 

Contact us today to discuss library access, customization, and collaborative screening strategies tailored to your research needs.

 

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Key References

1. Yong Chen, Xue Yuan, Wanhua Zhang, Minghai Tang, Li Zheng, Fang Wang, Wei Yan, Shengyong Yang, Yuquan Wei, Jun He, Lijuan Chen. Discovery of Novel Dual Histone Deacetylase and Mammalian Target of Rapamycin Target Inhibitors as a Promising Strategy for Cancer Therapy. J. Med. Chem. 2019, Vol. 62(3), pp. 1577−1592, DOI: 10.1021/acs.jmedchem.8b01825.