Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of
Alejandro Speck-Planche1, M Natália D S Cordeiro1
1LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
Abstract:
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.
Insights
Researchers developed a computational method combining perturbation-theory machine learning (PTML) and fragment-based topological design (FBTD) to discover new anti-colorectal cancer agents. This approach successfully designed novel molecules with predicted activity against multiple cancer cell lines.
Area of Science:
- Computational chemistry
- Drug discovery
- Oncology
Background:
- Colorectal cancer presents significant mortality and incidence rates, necessitating novel therapeutic agents.
- Computational methods offer a promising avenue for accelerating the discovery of anticancer drugs.
- Phenotypic drug discovery requires efficient tools to identify compounds with desired biological activity.
Purpose of the Study:
- To computationally design and predict novel molecules with multi-cell inhibitory activity against colorectal cancer.
- To validate a combined approach of perturbation-theory machine learning (PTML) and fragment-based topological design (FBTD) for drug discovery.
- To accelerate the early-stage discovery of versatile anticancer agents.
Main Methods:
- Development of a perturbation-theory machine learning (PTML) model for predicting anti-colorectal cancer activity.
- Application of fragment-based topological design (FBTD) for physicochemical and structural interpretation of the PTML model.
- Virtual screening using the developed PTML model and the CLC-Pred 2.0 webserver.
Main Results:
- The PTML model achieved over 80% sensitivity and specificity in training and test sets.
- The FBTD approach provided insights for rational drug design.
- Six novel, drug-like molecules were designed and predicted as active against multiple colorectal cancer cell lines.
Conclusions:
- The integrated PTML and FBTD methodology shows significant potential for the computer-aided de novo design of anticancer agents.
- This unified computational approach can accelerate early phenotypic drug discovery for colorectal cancer.
- The designed molecules represent promising candidates for further investigation as anti-colorectal cancer therapeutics.
