Machine learning for potential anti-cancer discovery from black sea cucumbers

Abstract

Despite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug.