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BACKGROUND: Traditional oriental medicines (TOMs) are a medical practice that follows different philosophies to pharmaceutical drugs and they have been in use for many years in different parts of the world. In this study, by integrating TOM formula and pharmaceutical drugs, we performed target space analysis between TOM formula target space and small-molecule drug target space. To do so, we manually curated 46 TOM formulas that are known to treat Anxiety, Diabetes mellitus, Epilepsy, Hypertension, Obesity, and Schizophrenia. Then, we employed Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties such as human ether-a-go-go related gene (hERG) inhibition, Carcinogenicity, and AMES toxicity to filter out potentially toxic herbal ingredients. The target space analysis was performed between TOM formula and small-molecule drugs: (i) both are known to treat the same disease, and (ii) each known to treat different diseases. Statistical significance of the overlapped target space between the TOM formula and small-molecule drugs was measured using support value. Support value distribution from randomly selected target space was calculated to validate the result. Furthermore, the Si-Wu-Tang (SWT) formula and published literature were also used to evaluate our results. RESULT: This study tried to provide scientific evidence about the effectiveness of the TOM formula to treat the main indication with side effects that could come from the use of small-molecule drugs. The target space analysis between TOM formula and small-molecule drugs in which both are known to treat the same disease shows that many targets overlapped between the two medications with a support value of 0.84 and weighted average support of 0.72 for a TOM formula known to treat Epilepsy. Furthermore, support value distribution from randomly selected target spaces in this analysis showed that the number of overlapped targets is much higher between TOM formula and small-molecule drugs that are known to treat the same disease than in randomly selected target spaces. Moreover, scientific literature was also used to evaluate the medicinal efficacy of individual herbs. CONCLUSION: This study provides an evidence to the effectiveness of a TOM formula to treat the main indication as well as side effects associated with the use of pharmaceutical drugs, as demonstrated through target space analysis.

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Small molecules with multitarget activity are capable of triggering polypharmacological effects and are of high interest in drug discovery. Compared to single-target compounds, promiscuity also affects drug distribution and pharmacodynamics and alters ADMET characteristics. Features distinguishing between compounds with single- and multitarget activity are currently only little understood. On the basis of systematic data analysis, we have assembled large sets of promiscuous compounds with activity against related or functionally distinct targets and the corresponding compounds with single-target activity. Machine learning predicted promiscuous compounds with surprisingly high accuracy. Molecular similarity analysis combined with control calculations under varying conditions revealed that accurate predictions were largely determined by structural nearest-neighbor relationships between compounds from different classes. We also found that large proportions of promiscuous compounds with activity against related or unrelated targets and corresponding single-target compounds formed analog series with distinct chemical space coverage, which further rationalized the predictions. Moreover, compounds with activity against proteins from functionally distinct classes were often active against unique targets that were not covered by other promiscuous compounds. The results of our analysis revealed that nearest-neighbor effects determined the prediction of promiscuous compounds and that preferential partitioning of compounds with single- and multitarget activity into structurally distinct analog series was responsible for such effects, hence providing a rationale for the presence of different structure-promiscuity relationships.

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Background: Polypharmacology is emerging as the next paradigm in drug discovery. However, considerable challenges still exist for polypharmacology modeling. In this study, we developed a rational design to identify highly potential targets (HPTs) for polypharmacological drugs, such as berberine. Methods and Results: All the proven co-crystal structures locate berberine in the active cavities of a redundancy of aromatic, aliphatic, and acidic residues. The side chains from residues provide hydrophobic and electronic interactions to aid in neutralization for the positive charge of berberine. Accordingly, we generated multi-target binding motifs (MBM) for berberine, and established a new mathematical model to identify HPTs based on MBM. Remarkably, the berberine MBM was embodied in 13 HPTs, including beta-secretase 1 (BACE1) and amyloid-ß1-42 (Aß1-42). Further study indicated that berberine acted as a high-affinity BACE1 inhibitor and prevented Aß1-42 aggregation to delay the pathological process of Alzheimer's disease. Conclusion: Here, we proposed a MBM-based drug-target space model to analyze the underlying mechanism of multi-target drugs against polypharmacological profiles, and demonstrated the role of berberine in Alzheimer's disease. This approach can be useful in derivation of rules, which will illuminate our understanding of drug action in diseases.