Medicinal plants contain inherently active ingredients. Such ingredients are beneficial to prevent and cure diseases, as well as to perform specific biological functions. In contrast to synthetic drugs, which is based on one single chemicals, medicinal plants exert their beneficial effects through the additive or synergistic action of several chemical compounds. Those chemical compound act on single or multiple targets (multicomponent therapeutic) associated with a physiological process. Active ingredients combinations show a synergistic effect. This means that the combinational effect of several active ingredients is greater than that of individual one acting separately. A network target can be used to identify synergistic effects of plants active ingredients. The method of NIMS (Network target-based Identification of Multicomponent Synergy) is a computational approach to identify the potential synergistics effect of active ingredients. It also assessess synergistic strength of any active ingradients at the molecular level by synergy scores. We investigate these synergistic on a Jamu formula for diabetes mellitus type 2.  The Jamu formula is composed of four medicinal plants, namely Tinospora crispa , Zingiber officinale, Momordica charantia, and Blumea balsamivera. Our work succesfully demonstrates that the highest synergy scores on medicinal plants synergy can be seen in pairs of several active ingredients in Zingiber officinale. On the other hand, the synergy of pairs of active ingredients in Momordica charantia and Zingiber officinale posseses a relatively high score. The same occurs in Tinospora crispa and Zingiber officinale.

Medicinal plants contain inherently active ingredients. Such ingredients are beneficial to prevent and cure diseases, as well as to perform specific biological functions. In contrast to synthetic drugs, which is based on one single chemicals, medicinal plants exert their beneficial effects through the additive or synergistic action of several chemical compounds. Those chemical compound act on single or multiple targets (multicomponent therapeutic) associated with a physiological process. Active ingredients combinations show a synergistic effect. This means that the combinational effect of several active ingredients is greater than that of individual one acting separately. A network target can be used to identify synergistic effects of plants active ingredients. The method of NIMS (Network target-based Identification of Multicomponent Synergy) is a computational approach to identify the potential synergistics effect of active ingredients. It also assessess synergistic strength of any active ingradients at the molecular level by synergy scores. We investigate these synergistic on a Jamu formula for diabetes mellitus type 2.  The Jamu formula is composed of four medicinal plants, namely Tinospora crispa , Zingiber officinale, Momordica charantia, and Blumea balsamivera. Our work succesfully demonstrates that the highest synergy scores on medicinal plants synergy can be seen in pairs of several active ingredients in Zingiber officinale. On the other hand, the synergy of pairs of active ingredients in Momordica charantia and Zingiber officinale posseses a relatively high score. The same occurs in Tinospora crispa and Zingiber officinale.

Jamu is the Indonesian traditional herbal medicine composed of several plants that have been practised for many centuries to maintain good health and to treat diseases. Unlike chemical drugs that have only one single target, the components of jamu have multiple channels and targets. Therefore, the identification of potential target and their actions is a major challenge in jamu research. We used a computational method called drugCIPHER-CS to predict the target profile for each herbal ingredient from jamu formula by combining the drug chemical interaction information in a heterogeneous network that correlates chemical, pharmacological, and genomic spaces. We also analysed network target to evaluate the importance protein of compound and pharmacological actions to help explain the action mechanisms of the jamu formula. In this work, we presented a case study of antidiabetic Jamu formula we previously developed. The jamu formula composed of four medicinal plants, namely Tinospora crispa, Zingiber officinale, Momordica charantia, and Blumea balsamifera. After determining 55 bioactive compounds in the jamu formula, we used DrugCIPHER-CS to predict their potential targets. The top 100 target profiles predicted for each compound were selected, and the target profiles of all collected compounds within a medicinal plant were combined to form integrative target profiles of this formula. We obtained 1250 potential targets of the jamu formula among 9500 listed targets on protein-protein interactions data. The potential targets then are involved in the protein interaction network and measured their topological networks by combining their degree, centrality, and betweenness. The network topology showed 20 unique potential targets hit by four medicines in jamu formula. DrugCHIPER-CS as a computational approach can speed the genome-wide identification of drug target on jamu formula efficiently. The potential target in each compound and the network demonstrate new insight to understand the mechanism on Jamu formula. However, experimental validation is still needed.

Resistance to antibiotics is increasing to alarmingly high levels. As antibiotics are less effective, more infections are becoming more complex and often impossible to treat. Numerous antibiotics discovered in marine organisms show that the marine environment, which accounts for over half of the world's biodiversity, is a massive source for novel antibiotics and that this resource must be explored to identify next-generation antibiotics. This research aimed to predict antibacterial activity in marine compounds using a computational approach to reduce the cost and time of finding marine organisms, extracting, and testing numerous unknown marine compounds' bioactivities. We used a simple unsupervised learning approach to predict the biological activity of marine compounds using agglomerative hierarchical clustering. We mixed antibiotic drug data in DrugBank Database and chemical compound data from marine organisms in literature to compile our dataset. We applied five linkage methods in our dataset and compared the best method by assessing internal validation measurement. We found that the Ward with squared dissimilarity matrix is the best method in the dataset, and ten compounds from 73 compounds of the marine compound are determined as potential marine compounds which have antibacterial activity.

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known.