title: Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents.
abstract: Cancer cells have upregulated DNA repair mechanisms, enabling them survive DNA damage induced during repeated rapid cell divisions and targeted chemotherapeutic treatments. Cancer cell proliferation and survival targeting via inhibition of DNA repair pathways is currently a very promiscuous anti-tumor approach. The deubiquitinating enzyme, USP1 is known to promote DNA repair via complexing with UAF1. The USP1/UAF1 complex is responsible for regulating DNA break repair pathways such as trans-lesion synthesis pathway, Fanconi anemia pathway and homologous recombination. Thus, USP1/UAF1 inhibition poses as an efficient anti-cancer strategy. The recently made available high throughput screen data for anti USP1/UAF1 activity prompted us to compute bioactivity predictive models that could help in screening for potential USP1/UAF1 inhibitors having anti-cancer properties. The current study utilizes publicly available high throughput screen data set of chemical compounds evaluated for their potential USP1/UAF1 inhibitory effect. A machine learning approach was devised for generation of computational models that could predict for potential anti USP1/UAF1 biological activity of novel anticancer compounds. Additional efficacy of active compounds was screened by applying SMARTS filter to eliminate molecules with non-drug like features. The structural fragment analysis was further performed to explore structural properties of the molecules. We demonstrated that modern machine learning approaches could be efficiently employed in building predictive computational models and their predictive performance is statistically accurate. The structure fragment analysis revealed the structures that could play an important role in identification of USP1/UAF1 inhibitors.
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067 India.
authors.forename: Divya
authors.initials: D
authors.lastname: Wahi
authors.affiliation: Department of Bioscience and Biotechnology, Banasthali University, Tonk, 304022 Rajasthan India.
authors.forename: Salma
authors.initials: S
authors.lastname: Jamal
authors.affiliation: Department of Bioscience and Biotechnology, Banasthali University, Tonk, 304022 Rajasthan India.
authors.forename: Sukriti
authors.initials: S
authors.lastname: Goyal
authors.affiliation: Department of Biotechnology, TERI University, Plot No. 10, Institutional Area, Vasant Kunj, New Delhi, 110 070 India.
authors.forename: Aditi
authors.initials: A
authors.lastname: Singh
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067 India.
authors.forename: Ritu
authors.initials: R
authors.lastname: Jain
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067 India.
authors.forename: Preeti
authors.initials: P
authors.lastname: Rana
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067 India.
authors.forename: Abhinav
authors.initials: A
authors.lastname: Grover
keywords: Anticancer; Cancer; Cheminformatics; DNA repair; Inhibitors; Machine learning; Model; UAF1; USP1