Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

Many drugs are nature derived. Low drug productivity has renewed interest in natural products as drug-discovery sources. Nature-derived drugs are composed of dozens of molecular scaffolds generated by specific secondary-metabolite gene clusters in selected species. It can be hypothesized that drug-like structures probably are distributed in selective groups of species. We compared the species origins of 939 approved and 369 clinical-trial drugs with those of 119 preclinical drugs and 19,721 bioactive natural products. In contrast to the scattered distribution of bioactive natural products, these drugs are clustered into 144 of the 6,763 known species families in nature, with 80% of the approved drugs and 67% of the clinical-trial drugs concentrated in 17 and 30 drug-prolific families, respectively. Four lines of evidence from historical drug data, 13,548 marine natural products, 767 medicinal plants, and 19,721 bioactive natural products suggest that drugs are derived mostly from preexisting drug-productive families. Drug-productive clusters expand slowly by conventional technologies. The lack of drugs outside drug-productive families is not necessarily the result of under-exploration or late exploration by conventional technologies. New technologies that explore cryptic gene clusters, pathways, interspecies crosstalk, and high-throughput fermentation enable the discovery of novel natural products. The potential impact of these technologies on drug productivity and on the distribution patterns of drug-productive families is yet to be revealed.