Biological Evaluations, NMR Analyses, Molecular Modeling Studies, and Overview of the Synthesis of the Marine Natural Product (-)-Mucosin.

Natural products obtained from marine organisms continue to be a rich source of novel structural architecture and of importance in drug discovery, medicine, and health. However, the success of such endeavors depends on the exact structural elucidation and access to sufficient material, often by stereoselective total synthesis, of the isolated natural product of interest. (-)-Mucosin (1), a fatty acid derivative, previously presumed to contain a rare cis-bicyclo[4.3.0]non-3-ene moiety, has since been shown to be the trans-congener. Analytically, the fused bicyclic ring system in (-)-1 constitutes a particular challenge in order to establish its relative and absolute stereochemistry. Herein, data from biological evaluations, NMR and molecular modeling studies of (-)-1 are presented. An overview of the synthetic strategies enabling the exact structural elucidation of (-)-mucosin (1) is also presented.

Do adolescents and emerging adults receive the diabetes care they truly need? A nationwide study of the quality of diabetes health care during the transition from paediatric to adult care.

AIMS: The aim of this study was to assess the paediatric and adult diabetes care provided to adolescents and young adults with childhood-onset type 1 diabetes during the transition. METHODS: This nationwide population-based cohort study included 776 individuals with type 1 diabetes who were last registered in the Norwegian Childhood Diabetes Registry (NCDR) between 2009 and 2012 and had received adult health care for at least 2 years. The patients' experiences were reported in a validated questionnaire. Clinical data from the annual registrations in the NCDR were coupled with data from the medical records in adult diabetes care. The longitudinal measures of glycaemic control were analysed using a growth mixture model. RESULTS: A total of 321 young people answered the questionnaire and provided written informed consent for the collection of their data from their medical records. The mean age at transfer was 18.0 years (range = 15.0-23.5 years), and the mean age at participation was 22.7 years (range = 20.9-26.7 years). Significant differences (p < 0.001) in patient experiences were found between paediatric and adult diabetes care in several areas: contact with health-care personnel, continuity of care, interval between consultations and overall satisfaction. Registry and medical records data confirmed the patient-reported experiences. The longitudinal analyses identified two groups with distinctly different trajectories of glycaemic outcome over time. Patient-provider continuity and perceived preparedness for transfer were the most influential predictors. CONCLUSIONS: This study highlights several areas to be addressed for improving health care and the transition to adult diabetes care in adolescents and young adults with type 1 diabetes, including provider continuity, individualised care and involvement of multidisciplinary teams.

Impact of the COVID-19 pandemic on long-term trends in the prevalence of diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes: an international multicentre study based on data from 13 national diabetes registries.

BACKGROUND: An increased prevalence of diabetic ketoacidosis at diagnosis of type 1 diabetes in children was observed in various diabetes centres worldwide during the COVID-19 pandemic. We aimed to evaluate trends in the prevalence of diabetic ketoacidosis at diagnosis of paediatric type 1 diabetes before and during the COVID-19 pandemic, and to identify potential predictors of changes in diabetic ketoacidosis prevalence during the pandemic. METHODS: For this international multicentre study, we used data from 13 national diabetes registries (Australia, Austria, Czechia, Denmark, Germany, Italy, Luxembourg, New Zealand, Norway, Slovenia, Sweden, USA [Colorado], and Wales). The study population comprised 104 290 children and adolescents aged 6 months to younger than 18 years, who were diagnosed with type 1 diabetes between Jan 1, 2006, and Dec 31, 2021. The observed diabetic ketoacidosis prevalence in 2020 and 2021 was compared to predictions based on trends over the pre-pandemic years 2006-19. Associations between changes in diabetic ketoacidosis prevalence and the severity of the COVID-19 pandemic and containment measures were examined with excess all-cause mortality in the whole population and the Stringency Index from the Oxford COVID-19 Government Response Tracker. FINDINGS: 87 228 children and adolescents were diagnosed with type 1 diabetes between 2006 and 2019, 8209 were diagnosed in 2020, and 8853 were diagnosed in 2021. From 2006 to 2019, diabetic ketoacidosis at diagnosis of type 1 diabetes was present in 23 775 (27·3%) of 87 228 individuals and the mean annual increase in the prevalence of diabetic ketoacidosis in the total cohort from 2006 to 2019 was 1·6% (95% CI 1·3 to 1·9). The adjusted observed prevalence of diabetic ketoacidosis at diagnosis of type 1 diabetes was 39·4% (95% CI 34·0 to 45·6) in 2020 and 38·9% (33·6 to 45·0) in 2021, significantly higher than the predicted prevalence of 32·5% (27·8 to 37·9) for 2020 and 33·0% (28·3 to 38·5) for 2021 (p<0·0001 for both years). The prevalence of diabetic ketoacidosis was associated with the pandemic containment measures, with an estimated risk ratio of 1·037 (95% CI 1·024 to 1·051; p<0·0001) per ten-unit increase in the Stringency Index for 2020 and 1·028 (1·009 to 1·047; p=0·0033) for 2021, but was not significantly associated with excess all-cause mortality. INTERPRETATION: During the COVID-19 pandemic, there was a marked exacerbation of the pre-existing increase in diabetic ketoacidosis prevalence at diagnosis of type 1 diabetes in children. This finding highlights the need for early and timely diagnosis of type 1 diabetes in children and adolescents. FUNDING: German Federal Ministry for Education and Research, German Robert Koch Institute, German Diabetes Association, German Diabetes Foundation, Slovenian Research Agency, Welsh Government, Central Denmark Region, and Swedish Association of Local Authorities and Regions.

A collaborative comparison of international pediatric diabetes registries.

BACKGROUND: An estimated 1.1 million children and adolescents aged under 20 years have type 1 diabetes worldwide. Principal investigators from seven well-established longitudinal pediatric diabetes registries and the SWEET initiative have come together to provide an international collaborative perspective and comparison of the registries. WORK FLOW: Information and data including registry characteristics, pediatric participant clinical characteristics, data availability and data completeness from the Australasian Diabetes Data Network (ADDN), Danish Registry of Childhood and Adolescent Diabetes (DanDiabKids), Diabetes prospective follow-up registry (DPV), Norwegian Childhood Diabetes Registry (NCDR), National Paediatric Diabetes Audit (NPDA), Swedish Childhood Diabetes Registry (Swediabkids), T1D Exchange Quality Improvement Collaborative (T1DX-QI), and the SWEET initiative was extracted up until 31 December 2020. REGISTRY OBJECTIVES AND OUTCOMES: The seven diabetes registries and the SWEET initiative collectively show data of more than 900 centers and around 100,000 pediatric patients, the majority with type 1 diabetes. All share the common objectives of monitoring treatment and longitudinal outcomes, promoting quality improvement and equality in diabetes care and enabling clinical research. All generate regular benchmark reports. Main differences were observed in the definition of the pediatric population, the inclusion of adults, documentation of CGM metrics and collection of raw data files as well as linkage to other data sources. The open benchmarking and access to regularly updated data may prove to be the most important contribution from registries. This study describes aspects of the registries to enable future collaborations and to encourage the development of new registries where they do not exist.

Polygenic overlap and shared genetic loci between loneliness, severe mental disorders, and cardiovascular disease risk factors suggest shared molecular mechanisms.

Clinical and epidemiological evidence suggest that loneliness is associated with severe mental disorders (SMDs) and increases the risk of cardiovascular disease (CVD). However, the mechanisms underlying the relationship between loneliness, SMDs, and CVD risk factors remain unknown. Here we explored overlapping genetic architecture and genetic loci shared between SMDs, loneliness, and CVD risk factors. We analyzed large independent genome-wide association study data on schizophrenia (SCZ), bipolar disorder (BD), major depression (MD), loneliness and CVD risk factors using bivariate causal mixture mode (MiXeR), which estimates the total amount of shared variants, and conditional false discovery rate to evaluate overlap in specific loci. We observed substantial genetic overlap between SMDs, loneliness and CVD risk factors, beyond genetic correlation. We identified 149 loci jointly associated with loneliness and SMDs (MD n = 67, SCZ n = 54, and BD n = 28), and 55 distinct loci jointly associated with loneliness and CVD risk factors. A total of 153 novel loneliness loci were found. Most of the shared loci possessed concordant effect directions, suggesting that genetic risk for loneliness may increase the risk of both SMDs and CVD. Functional analyses of the shared loci implicated biological processes related to the brain, metabolic processes, chromatin and immune system. Altogether, the study revealed polygenic overlap between loneliness, SMDs and CVD risk factors, providing new insights into their shared genetic architecture and common genetic mechanisms.

Inhibitor binding to mutants of protein kinase A with GGGxxG and GxGxxA glycine-rich loop motifs.

The conserved GxGxxG motif of protein kinases forms a beta turn at the tip of the flexible glycine-rich loop and creates much of the ATP pocket binding surface. Notable exceptions to this sequence include GGGxxG in ABL kinase and GxGxxA in protein kinase C isoforms. We constructed the corresponding mutants of PKA, T51G, and G55A, and tested quinazoline inhibitors that were designed to bind via glycine-rich loop interactions, testing also staurosporine for comparison. The quinazoline inhibitors have significantly reduced binding strengths in both mutants. In striking contrast to these results, the binding of the "pan-kinome" inhibitor staurosporine is strengthened in the mutants. Surface plasmon resonance (SPR) shows that the tightened binding of staurosporine arises from increased kon rates, changes not offset by more moderately increased koff rates. The SPR results fit best to a two step binding process for staurosporine in wild type PKA, but not the mutants.

Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR.

MOTIVATION: Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. RESULTS: Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/precimed/mixer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Structural insight into a CE15 esterase from the marine bacterial metagenome.

The family 15 carbohydrate esterase (CE15) MZ0003, which derives from a marine Arctic metagenome, has a broader substrate scope than other members of this family. Here we report the crystal structure of MZ0003, which reveals that residues comprising the catalytic triad differ from previously-characterized fungal homologs, and resolves three large loop regions that are unique to this bacterial sub-clade. The catalytic triad of the bacterial CE15, which includes Asp 332 as its third member, closely resembles that of family 1 carbohydrate esterases (CE1), despite the overall lower structural similarity with members of this family. Two of the three loop regions form a subdomain that deepens the active site pocket and includes several basic residues that contribute to the high positive charge surrounding the active site. Docking simulations predict specific interactions with the sugar moiety of glucuronic-acid substrates, and with aromatically-substituted derivatives that serve as model compounds for the lignin-carbohydrate complex of plant cell walls. Molecular dynamics simulations indicate considerable flexibility of the sub-domain in the substrate-bound form, suggesting plasticity to accommodate different substrates is possible. The findings from this first reported structure of a bacterial member of the CE15 family provide insight into the basis of its broader substrate specificity.

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR.

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH-O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches.

In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Evaluating the predictivity of virtual screening for ABL kinase inhibitors to hinder drug resistance.

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. 'Dual active' inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies.

Structural origins of AGC protein kinase inhibitor selectivities: PKA as a drug discovery tool.

The era of structure-based protein kinase inhibitor design began in the early 1990s with the determination of crystal structures of protein kinase A (PKA, or cyclic AMP-dependent kinase). Although many other protein kinases have since been extensively characterized, PKA remains a prototype for studies of protein kinase active conformations. It serves well as a model for the structural properties of AGC subfamily protein kinases, clarifying inhibitor selectivity profiles. Its reliable expression, constitutive activity, simple domain structure, and reproducible crystallizability have also made it a useful surrogate for the discovery of inhibitors of both established and emerging AGC kinase targets.

1,2,3-triazole analogs of combretastatin A-4 as potential microtubule-binding agents.

A series of cis-restricted 1,4- and 1,5-disubstituted 1,2,3-triazole analogs of combretastatin A-4 (1) have been prepared. Cytotoxicity and tubulin inhibition studies showed that 2-methoxy-5-((5-(3,4,5-trimethoxyphenyl)-1H-1,2,3-triazol-1-yl)methyl)aniline (5e) and 2-methoxy-5-(1-(3,4,5-trimethoxybenzyl)-1H-1,2,3-triazol-5-yl)aniline (6e) were two of the most active compounds. Molecular modeling studies revealed that the N-2 and N-3 atoms in the triazole rings in 5e and 6e did not form hydrogen bonds with the amino acids in the anticipated pharmacophore.

Protein kinase inhibition of clinically important staurosporine analogues.

The isolation in 1977 of the microbial alkaloid staurosporine inaugurated research into several distinct series of related natural and synthetic compounds. This has especially included research into applications as anticancer drugs, beginning with the observation of low nanomolar inhibition of protein kinases. At present, several staurosporine cognates are in advanced clinical trials as anticancer agents, with the potential to join the 10 other protein kinase inhibitors now approved for clinical use. Staurosporine is a broadly selective and potent protein kinase inhibitor, with submicromolar binding to the vast majority of the protein kinases tested, and binding most of them more tightly than 100 nM. Crystal structures have shown the extended buried surface area interactions between the protein kinase adenine binding site and the extended aromatic plane of the inhibitor, together with protein-saccharide interactions in the ribose binding site. Together with structures of closely related analogues, there are now some 70 X-ray crystal structures in the Protein Data Bank that enable analysis of target binding properties of the clinical compounds. In this manuscript we review the discovery of these compounds, revisit crystal structures and review the observed interactions. These support the interpretation of kinase selectivity profiles of staurosporine and its analogues, including midostaurin (PKC412), for which a co-crystal structure is not yet available. Further, the mix of purely natural, biosynthetically and chemically modified compounds described here offer insights into prospects and strategies for drug discovery via bioprospecting.

Molecular recognition of docosahexaenoic acid by peroxisome proliferator-activated receptors and retinoid-X receptor alpha.

The family of peroxisome proliferator-activated receptors (PPARs) is the molecular target of synthetic antidiabetic and hypolipidemic drugs. The side effects of these drugs are limiting their use in patients with high lipid levels. Natural compounds, like Docosahexaenoic acid (DHA) from fish oil, have beneficial effects in the treatment of metabolic diseases, and several DHA derivatives are known to activate PPAR genes. Experimental studies on affinities of DHA and its derivatives for PPARs are not available. In the present study we are therefore using computational docking, molecular dynamics simulation, and several scoring programs to predict affinities and binding modes of DHA for PPARs and retinoid-X receptor alpha, which is the DNA binding partner of PPARs. The calculations indicated that DHA binds to PPARs and the retinoid-X receptor alpha with high affinity, and that different PPARs exhibited different structural effects on the first four carbons atoms of DHA. Our data indicate that the beneficial health effects of DHA may be obtained by high affinity binding to the PPARs.

Ligand-induced stabilization and activation of peroxisome proliferator-activated receptor gamma.

Peroxisome proliferator-activated receptor gamma belongs to the nuclear receptor superfamily and is activated by the antidiabetic drugs rosiglitazone and pioglitazone. Ligand-independent constitutive activity of peroxisome proliferator-activated receptor gamma is also demonstrated. X-ray crystallographic structures show that the active or inactive conformations of the receptor are determined by the position of helix 12 in the C-terminal end. In this study, molecular dynamics simulations were used to gain molecular insight into the activation process and the structural stability of inactive and active peroxisome proliferator-activated receptor gamma receptor structure. The simulations showed: (i) during molecular dynamics simulations without agonist at the active site, the receptor structure with helix 12 in a position corresponding to activated receptor structure was structurally more stable than with helix 12 in a position corresponding to inactive receptor structure, which may contribute to the constitutive activity of the receptor; (ii) docosahexenoic acid stabilized the active receptor conformation more efficiently than the glitazones; (iii) docosahexenoic acid, but not glitazones, induced structural changes into the inactive receptor structure such that helix 12 was shifted into a position more similar to that of an active receptor structure, which indicate that docosahexenoic acid is a more effective peroxisome proliferator-activated receptor gamma agonist than the glitazones.

1,5-Disubstituted 1,2,3-triazoles as cis-restricted analogues of combretastatin A-4: Synthesis, molecular modeling and evaluation as cytotoxic agents and inhibitors of tubulin.

A series of cis-restricted 1,5-disubstituted 1,2,3-triazole analogues of combretastatin A-4 (1) have been prepared. The triazole 12f, 2-methoxy-5-(1-(3,4,5-trimethoxyphenyl)-1H-1,2,3-triazol-5-yl)aniline, displayed potent cytotoxic activity against several cancer cell lines with IC(50) values in the nanomolar range. The ability of triazoles to inhibit tubulin polymerization has been evaluated, and 12f inhibited tubulin polymerization with IC(50)=4.8microM. Molecular modeling experiments involving 12f and the colchicine binding site of alpha,beta-tubulin showed that the triazole moiety interacts with beta-tubulin via hydrogen bonding with several amino acids.

Are fish oil omega-3 long-chain fatty acids and their derivatives peroxisome proliferator-activated receptor agonists?

BACKGROUND: Peroxisome proliferator-activated receptors (PPARalpha, PPARgamma, and PPARdelta) are physiological sensors for glucose and lipid homeostasis. They are also the targets of synthetic drugs; such as fibrates as PPARalpha agonists which lower lipid level, and glitazones as PPARgamma agonists which lower glucose level. As diabetes and metabolic diseases are often associated with high blood glucose and lipid levels, drugs that activate both PPARalpha/gamma would be a logical approach. But synthetically developed PPARalpha/gamma dual agonists and glitazones are showing side effects such as weight gain and edema. Therefore, natural compounds and their close derivatives are focused as future drugs against metabolic diseases. PRESENTATION OF HYPOTHESIS: Docosahexaenoic acid and eicosapentaenoic acid, which are the fatty acids abundant in fish oil, are traditionally used against metabolic diseases. These fatty acids act as PPAR agonists that transcript the genes involved in glucose and lipid homeostasis. Present hypothesis suggests that the derivatives of these fatty acids are stronger PPAR agonists than the parent compounds. X-ray structures of PPARs indicate that alpha or beta derivatives of fatty acids would fit into PPARalpha/gamma binding cavity. Therefore, the derivatives will exhibit stronger affinities and activities than the parent compounds. TESTING OF THE HYPOTHESIS: Ligand binding assays and gene transactivation assays should be performed to test the hypothesis. Fluorescence-based methods are advantageous in binding assays, because they were found more suitable for fatty acid binding assays. In transactivation assays, care should be taken to remove contaminants from recombinant proteins. IMPLICATIONS OF THE HYPOTHESIS: Present hypothesis is framed on the basis of molecular structure of natural PPAR agonists. Small structural changes in the molecular structure of fatty acids have a great influence on activating different PPARs. Therefore, this hypothesis bridges the concept of natural PPAR agonists and the use of structural information in designing new drugs against diabetes and metabolic syndrome. The derivatives may also be used as anti-inflammatory and anticancer agents.

Theoretical calculations of the catalytic triad in short-chain alcohol dehydrogenases/reductases.

Three highly conserved active site residues (Ser, Tyr, and Lys) of the family of short-chain alcohol dehydrogenases/reductases (SDRs) were demonstrated to be essential for catalytic activity and have been denoted the catalytic triad of SDRs. In this study computational methods were adopted to study the ionization properties of these amino acids in SDRs from Drosophila melanogaster and Drosophila lebanonensis. Three enzyme models, with different ionization scenarios of the catalytic triad that might be possible when inhibitors bind to the enzyme cofactor complex, were constructed. The binding of the two alcohol competitive inhibitors were studied using automatic docking by the Internal Coordinate Mechanics program, molecular dynamic (MD) simulations with the AMBER program package, calculation of the free energy of ligand binding by the linear interaction energy method, and the hydropathic interactions force field. The calculations indicated that deprotonated Tyr acts as a strong base in the binary enzyme-NAD(+) complex. Molecular dynamic simulations for 5 ns confirmed that deprotonated Tyr is essential for anchoring and orientating the inhibitors at the active site, which might be a general trend for the family of SDRs. The findings here have implications for the development of therapeutically important SDR inhibitors.

Signposts of docking and scoring in drug design.

Docking and scoring tools are often used in the early stages of drug discovery projects today. Because the language of this field of drug design often includes intermingled terms of physics, chemistry, biology, and medicine, the significant developments of docking and scoring are challenging to follow for non-experts. In this Science Philosophy article, I attempt to clarify the concepts used in docking and scoring to help articulate the successes and limitations of this multidisciplinary field in more comprehensible manner.