Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines.

Colorectal cancer is one of the most dangerous neoplastic diseases in terms of both mortality and incidence. Thus, anti-colorectal cancer agents are urgently needed. Computational approaches have great potential to accelerate the phenotypic discovery of versatile anticancer agents. Here, by combining perturbation-theory machine learning (PTML) modeling with the fragment-based topological design (FBTD) approach, we provide key computational evidence on the computer-aided de novo design and prediction of new molecules virtually exhibiting multi-cell inhibitory activity against different colorectal cancer cell lines. The PTML model created in this study achieved sensitivity and specificity values exceeding 80% in training and test sets. The FBTD approach was employed to physicochemically and structurally interpret the PTML model. These interpretations enabled the rational design of six new drug-like molecules, which were predicted as active against multiple colorectal cancer cell lines by both our PTML model and a CLC-Pred 2.0 webserver, with the latter being a well-established virtual screening tool for early anticancer discovery. This work confirms the potential of the joint use of PTML and FBTD as a unified computational methodology for early phenotypic anticancer drug discovery.