Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

Drug development is essential to the advancement of human health, however, the process is slow, costly, and at high risk of failure at all stages. A promising strategy for expediting and improving the probability of success in the drug development process is the use of naturally randomized human genetic variation for drug target identification and validation. These data can be harnessed using the Mendelian randomization (MR) analytic paradigm to proxy the lifelong consequences of genetic perturbations of drug targets. In this review, we discuss the myriad applications of the MR paradigm for human drug target identification and validation. We review the methodology and applications of MR, key limitations of MR, and potential future opportunities for research. Throughout the review, we refer to illustrative examples of MR analyses investigating the consequences of genetic inhibition of interleukin 6 signaling which, in some cases, have anticipated results from randomized controlled trials. As human genetic data become more widely available, we predict that MR will serve as a key pillar of support for drug development efforts.

Tetraspanins are a superfamily of transmembrane proteins that in flatworms have structural roles in the development, maturation or stability of the tegument. Several tetraspanins are considered as potential candidates for vaccines or drugs against helminths. Monopisthocotylean monogeneans are ectoparasites of fish that are health hazards for farmed fish. The aim of this study was to identify in silico putative tetraspanins in the genomic datasets of four monopisthocotylean species. The analysis predicted and classified 40 tetraspanins in Rhabdosynochus viridisi, 39 in Scutogyrus longicornis, 22 in Gyrodactylus salaris and 13 in Neobenedenia melleni, belonging to 13 orthologous groups. The high divergence of tetraspanins made it difficult to annotate their function. However, a conserved group was identified in different metazoan taxa. According to this study, metazoan tetraspanins can be divided into 17 monophyletic groups. Of the 114 monogenean tetraspanins, only seven were phylogenetically close to tetraspanins from non-platyhelminth metazoans, which suggests that this group of proteins shows rapid sequence divergence. The similarity of the monopisthocotylean tetraspanins was highest with trematodes, followed by cestodes and then free-living platyhelminths. In total, 27 monopisthocotylean-specific and 34 flatworm-specific tetraspanins were identified. Four monogenean tetraspanins were orthologous to TSP-1, which is a candidate for the development of vaccines and a potential pharmacological target in trematodes and cestodes. Although studies of tetraspanins in parasitic flatworms are scarce, this is an interesting group of proteins for the development of new methods to control monogeneans.

Unavailability of vaccines and effective drugs are primarily responsible for the growing menace of cryptosporidiosis. This study has incorporated a bioinformatics-based screening approach to explore potential vaccine candidates and novel drug targets in Cryptosporidium parvum proteome. A systematic strategy was defined for comparative genomics, orthology with related Cryptosporidium species, prioritization parameters and MHC class I and II binding promiscuity. The approach reported cytoplasmic protein cgd7_1830, a signal peptide protein, as a novel drug target. SWISS-MODEL online server was used to generate the 3D model of the protein and was validated by PROCHECK. The model has been subjected to in silico docking study with screened potent lead compounds from the ZINC database, PubChem and ChEMBL database using Flare software package of Cresset®. Furthermore, the approach reported protein cgd3_1400, as a vaccine candidate. The predicted B- and T-cell epitopes on the proposed vaccine candidate with highest scores were also subjected to docking study with MHC class I and II alleles using ClusPro web server. Results from this study could facilitate selection of proteins which could serve as drug targets and vaccine candidates to efficiently tackle the growing threat of cryptosporidiosis.

Sphingolipids (SLs) are an integral part of all eukaryotic cellular membranes. In addition, they have indispensable functions as signalling molecules controlling a myriad of cellular events. Disruption of either the de novo synthesis or the degradation pathways has been shown to have detrimental effects. The earlier identification of selective inhibitors of fungal SL biosynthesis promised potent broad-spectrum anti-fungal agents, which later encouraged testing some of those agents against protozoan parasites. In this review we focus on the key enzymes of the SL de novo biosynthetic pathway in protozoan parasites of the Apicomplexa and Kinetoplastidae, outlining the divergence and interconnection between host and pathogen metabolism. The druggability of the SL biosynthesis is considered, alongside recent technology advances that will enable the dissection and analyses of this pathway in the parasitic protozoa. The future impact of these advances for the development of new therapeutics for both globally threatening and neglected infectious diseases is potentially profound.

Plasmodium falciparum thymidylate kinase (PfTMK) can tolerate a range of substrates, which distinguishes it from other thymidylate kinases. The enzyme not only phosphorylates TMP and dUMP but can also tolerate bulkier purines, namely, dGMP, GMP, and dIMP. In order to probe the flexibility of PfTMK in accommodating ligands of various sizes, we developed 6 mutant enzymes and subjected these to thermodynamic, inhibitory and catalytic evaluation. Kinase activity was markedly affected by introducing a larger lysine residue instead of A111. The lack of the hydroxyl group after inducing mutation of Y107F affected enzyme activity, and had a more severe impact on dGMP kinase activity. PfTMK can be inhibited by both purine and pyrimidine nucleosides, raising the possibility of developing highly selective drugs. Thermodynamic analysis revealed that enthalpic forces govern both purine and pyrimidine nucleoside monophosphate binding, and the binding affinity of both substrates was highly comparable. The heat produced due to dGMP binding is lower than that attributable to TMP. This indicates that additional interactions occur with TMP, which may be lost with larger dGMP. Targeting PfTMK not only affects thymidine nucleotide synthesis but may also affect purine nucleotides, and thus the enzyme represents an attractive antimicrobial target.