In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

Abstract

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

Therapeutic value of steroidal alkaloids in cancer: Current trends and future perspectives

“…Computer‐aided drug design approaches are a promising area for the development of anticancer drug‐like compounds using SA scaffolds. To avoid the huge costs and labor intensiveness incurred with random screening, different types of in silico approaches, including virtual screening, molecular modeling, molecular docking, QSAR, pharmacophore modeling, high‐throughput screening, and cloud computing, are efficient and effective for anticancer drug discovery campaigns using SAs . This technology has changed the scenario of rational design in the anticancer drug discovery process.…”

“…To avoid the huge costs and labor intensiveness incurred with random screening, different types of in silico approaches, including virtual screening, molecular modeling, molecular docking, QSAR, pharmacophore modeling, highthroughput screening, and cloud computing, are efficient and effective for anticancer drug discovery campaigns using SAs. [150][151][152] This technology has changed the scenario of rational design in the anticancer drug discovery process. Various web-based programs for QSAR [JRC QSAR Model Database, DemQSAR, OCHEM (Online Chemical Modeling Environment), and MC-3DQSAR] are available for the generation of potential leads.…”

Anti-HIV activity prediction and enrichment identify novel human targets for Anti-HIV therapy

“…The RFW_FP model was generated as follows. Relative Frequency-Weighted Fingerprint (RFW_FP) was used in our previous study and powerful to distinguish the active and inactive compounds for anti-cancer 24,36,37 . Firstly, RFW_FP was used to calculate the compound fingerprints as follows: α is the amplifying factor.…”

Efficient repositioning of approved drugs as anti-HIV agents using Anti-HIV-Predictor

“…RFW_FP was first used in our previous study and powerful to distinguish the active and inactive compounds for anti-cancer 29,30 . The other two models are Support Vector Machine (SVM) and Random Forest (RF) models.…”

“…RFW_FP is a novel molecular description method which considers the frequency of bit in active and inactive datasets and integrates it to each compound fingerprint. RFW_FP was first used in our previous study and powerful to distinguish the active and inactive compounds for anti-cancer 29,30 . The other two models are Support Vector Machine (SVM) and Random Forest (RF) models.…”