PENERAPAN PENDEKATAN MACHINE LEARNING PADA PENGEMBANGAN BASIS DATA HERBAL SEBAGAI SUMBER INFORMASI KANDIDAT OBAT KANKER

Cancer is still an epidemiological disease in Indonesia. Drug development against cancer still relies to pharmacological laboratories and natural chemicals, which could have side effects. Cancer drug development has entered the stage of molecular biology, where the interaction of ligand chemical structure with receptor protein can be studied with high accuracy. Various chemical compounds, ranging from synthetic, semi-synthetic, to natural materials, developed for the purpose to fight one of the most dangerous diseases. In the context of the development of herbal-based drugs, there has been found heaps of natural compounds, curated and annotated, in various databases belonging to China, Taiwan, Indonesia, Japan, and several other countries. However, problems arise when choosing the best bioactive compounds to develop against cancer. Complexity arises because the metabolic pathway of cancer is very diverse, depending on the type and phase of cancer. Therefore, in this systematic review, we developed a machine learning approach to screen for these bioactive compounds, then took the best candidates for molecular simulation operations that would be tested for validity in wet experiments. Thus, the automation of the candidate drug development process for cancer could be achieved with great significance. It is known that the most effective and efficient machine learning method was Naïve Bayes, but the best in processing large amounts of compound data was classfier SVM. The future of complex bioactive compounds data could be secured by employing deep learning method. Keywords: machine learning, drug development, natural material compounds, metabolic pathways, cancer

Improved Machine Learning Models for Predicting Selective Compounds

Abstract The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step towards successful drug discovery. However, there still lacks efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multi-task learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out non-selective compounds. The multi-task method incorporates both activity and selectivity models into one multi-task model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multi-task methods significantly improve the selectivity prediction performance

Improved Machine Learning Models for Predicting Selective Compounds

The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance