Development of decision trees to discriminate HDAC8 inhibitors and non-inhibitors using recursive partitioning

Abstract

Histone deacetylase 8 (HDAC8) is involved in malignancy. Overexpression of HDAC8 is correlated with various cancers. Design of selective HDAC8 inhibitors is always a challenging task to the chemistry audiences. In this communication, a diverse set comprising large number of compounds are subjected to recursive partitioning (RP) analysis to develop decision trees to discriminate compounds into HDAC8 inhibitors (active) and non-inhibitors (inactive). Acquiring knowledge about the essential structural and physicochemical parameters can be useful in designing potential and selective HDAC8 inhibitors. Moreover, this work validates our previous results observed in Bayesian modelling study of this dataset. This comparative learning will surely enrich drug discovery aspects related to HDAC8 inhibitors.

Communicated by Ramaswamy H. Sarma

Keywords:

• Cancer
• HDAC8 inhibitor
• recursive partitioning model
• molecular fingerprint
• ECFP_6
• FCFP_6
• decision tree

Acknowledgements

Sk. Abdul Amin sincerely acknowledges Council of Scientific & Industrial Research (CSIR), New Delhi for awarding the Senior Research Fellowship [FILE NO.: 09/096(0967)/2019-EMR-I, Dated: 01-04-2019]. Nilanjan Adhikari is thankful to Council of Scientific & Industrial Research (CSIR), New Delhi for awarding the Research Associate (RA) Fellowship [FILE NO.: 09/096(0966)/2019-EMR-I, Dated: 28-03-2019]. We also thank the support from Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India for providing the research facilities.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Tarun Jha is thankful for the financial support from RUSA 2.0 and UPE Phase II Programmes of UGC, New Delhi to Jadavpur University, Kolkata, India.