Algorithm for the Pruning of Synthesis Graphs.

Synthesis route planning is in the core of chemical intelligence that will power the autonomous chemistry platforms. In this task, we rely on algorithms to generate possible synthesis routes with the help of retro- and forward-synthetic approaches. Generated synthesis routes can be merged into a synthesis graph which represents theoretical pathways to the target molecule. However, it is often required to modify a synthesis graph due to typical constraints. These constraints might include "undesirable substances", e.g., an intermediate that the chemist does not favor or substances that might be toxic. Consequently, we need to prune the synthesis graph by the elimination of such undesirable substances. Synthesis graphs can be represented as directed (not necessarily acyclic) bipartite graphs, and the pruning of such graphs in the light of a set of undesirable substances has been an open question. In this study, we present the Synthesis Graph Pruning (SGP) algorithm that addresses this question. The input to the SGP algorithm is a synthesis graph and a set of undesirable substances. Furthermore, information for substances is provided as metadata regarding their availability from the inventory. The SGP algorithm operates with a simple local rule set, in order to determine which nodes and edges need to be eliminated from the synthesis graph. In this study, we present the SGP algorithm in detail and provide several case studies that demonstrate the operation of the SGP algorithm. We believe that the SGP algorithm will be an essential component of computer aided synthesis planning.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Drug effect prediction by polypharmacology-based interaction profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.

A target-agnostic screen identifies approved drugs to stabilize the endoplasmic reticulum-resident proteome.

Endoplasmic reticulum (ER) dysregulation is associated with pathologies including neurodegenerative, muscular, and diabetic conditions. Depletion of ER calcium can lead to the loss of resident proteins in a process termed exodosis. To identify compounds that attenuate the redistribution of ER proteins under pathological conditions, we performed a quantitative high-throughput screen using the Gaussia luciferase (GLuc)-secreted ER calcium modulated protein (SERCaMP) assay, which monitors secretion of ER-resident proteins triggered by calcium depletion. We identify several clinically used drugs, including bromocriptine, and further characterize them using assays to measure effects on ER calcium, ER stress, and ER exodosis. Bromocriptine elicits protective effects in cell-based models of exodosis as well as in vivo models of stroke and diabetes. Bromocriptine analogs with reduced dopamine receptor activity retain similar efficacy in stabilizing the ER proteome, indicating a non-canonical mechanism of action. This study describes a strategic approach to identify small-molecule drugs capable of improving ER proteostasis in human disease conditions.

Relating the shape of protein binding sites to binding affinity profiles: is there an association?

BACKGROUND: Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses. RESULTS: PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors. CONCLUSIONS: This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.

SmartGraph: a network pharmacology investigation platform.

MOTIVATION: Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. RESULTS: The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. AVAILABILITY: https://smartgraph.ncats.io/.

A Perspective on Innovating the Chemistry Lab Bench.

Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.

Identification of a G-Protein-Independent Activator of GIRK Channels.

G-protein-gated inwardly rectifying K+ (GIRK) channels are essential effectors of inhibitory neurotransmission in the brain. GIRK channels have been implicated in diseases with abnormal neuronal excitability, including epilepsy and addiction. GIRK channels are tetramers composed of either the same subunit (e.g., homotetramers) or different subunits (e.g., heterotetramers). Compounds that specifically target subsets of GIRK channels in vivo are lacking. Previous studies have shown that alcohol directly activates GIRK channels through a hydrophobic pocket located in the cytoplasmic domain of the channel. Here, we report the identification and functional characterization of a GIRK1-selective activator, termed GiGA1, that targets the alcohol pocket. GiGA1 activates GIRK1/GIRK2 both in vitro and in vivo and, in turn, mitigates the effects of a convulsant in an acute epilepsy mouse model. These results shed light on the structure-based development of subunit-specific GIRK modulators that could provide potential treatments for brain disorders.

Modulation of Triple Artemisinin-Based Combination Therapy Pharmacodynamics by Plasmodium falciparum Genotype.

The first-line treatments for uncomplicated Plasmodium falciparum malaria are artemisinin-based combination therapies (ACTs), consisting of an artemisinin derivative combined with a longer acting partner drug. However, the spread of P. falciparum with decreased susceptibility to artemisinin and partner drugs presents a significant challenge to malaria control efforts. To stem the spread of drug resistant parasites, novel chemotherapeutic strategies are being evaluated, including the implementation of triple artemisinin-based combination therapies (TACTs). Currently, there is limited knowledge on the pharmacodynamic and pharmacogenetic interactions of proposed TACT drug combinations. To evaluate these interactions, we established an in vitro high-throughput process for measuring the drug concentration-response to three distinct antimalarial drugs present in a TACT. Sixteen different TACT combinations were screened against 15 parasite lines from Cambodia, with a focus on parasites with differential susceptibilities to piperaquine and artemisinins. Analysis revealed drug-drug interactions unique to specific genetic backgrounds, including antagonism between piperaquine and pyronaridine associated with gene amplification of plasmepsin II/III, two aspartic proteases that localize to the parasite digestive vacuole. From this initial study, we identified parasite genotypes with decreased susceptibility to specific TACTs, as well as potential TACTs that display antagonism in a genotype-dependent manner. Our assay and analysis platform can be further leveraged to inform drug implementation decisions and evaluate next-generation TACTs.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

MOTIVATION: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. RESULTS: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. AVAILABILITY: https://neo4covid19.ncats.io.

Better living through chemistry: Addressing emerging antibiotic resistance.

The increasing emergence of multidrug-resistant bacteria is recognized as a major threat to human health worldwide. While the use of small molecule antibiotics has enabled many modern medical advances, it has also facilitated the development of resistant organisms. This minireview provides an overview of current small molecule drugs approved by the US Food and Drug Administration (FDA) for use in humans, the unintended consequences of antibiotic use, and the mechanisms that underlie the development of drug resistance. Promising new approaches and strategies to counter antibiotic-resistant bacteria with small molecules are highlighted. However, continued public investment in this area is critical to maintain an edge in our evolutionary "arms race" against antibiotic-resistant microorganisms. Impact statement The alarming increase in antibiotic-resistant microorganisms is a rapidly emerging threat to human health throughout the world. Historically, small molecule drugs have played a major role in controlling bacterial infections and they continue to offer tremendous potential in countering resistant organisms. This minireview provides a broad overview of the relevant issues, including the diversity of FDA-approved small molecule drugs and mechanisms of drug resistance, unintended consequences of antibiotic use, the current state of development for small molecule antibacterials and financial challenges that impact progress towards novel therapies. The content will be informative to diverse stakeholders, including clinicians, basic scientists, translational scientists and policy makers, and may be used as a bridge between these key players to advance the development of much-needed therapeutics.

Unexplored therapeutic opportunities in the human genome.

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

Unexplored therapeutic opportunities in the human genome.

This corrects the article DOI: 10.1038/nrd.2018.14.

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes.

BACKGROUND: Complex network theory based methods and the emergence of "Big Data" have reshaped the terrain of investigating structure-activity relationships of molecules. This change gave rise to new methods which need to face an important challenge, namely: how to restructure a large molecular dataset into a network that best serves the purpose of the subsequent analyses. With special focus on network clustering, our study addresses this open question by proposing a data transformation method and a clustering framework. RESULTS: Using the WOMBAT and PubChem MLSMR datasets we investigated the relation between varying the similarity threshold applied on the similarity matrix and the average clustering coefficient of the emerging similarity-based networks. These similarity networks were then clustered with the InfoMap algorithm. We devised a systematic method to generate so-called "pseudo-reference" clustering datasets which compensate for the lack of large-scale reference datasets. With help from the clustering framework we were able to observe the effects of varying the similarity threshold and its consequence on the average clustering coefficient and the clustering performance. CONCLUSIONS: We observed that the average clustering coefficient versus similarity threshold function can be characterized by the presence of a peak that covers a range of similarity threshold values. This peak is preceded by a steep decline in the number of edges of the similarity network. The maximum of this peak is well aligned with the best clustering outcome. Thus, if no reference set is available, choosing the similarity threshold associated with this peak would be a near-ideal setting for the subsequent network cluster analysis. The proposed method can be used as a general approach to determine the appropriate similarity threshold to generate the similarity network of large-scale molecular datasets.

Erratum to: Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes.

[This corrects the article DOI: 10.1186/s13321-016-0127-5.].

Breaking the hierarchy--a new cluster selection mechanism for hierarchical clustering methods.

BACKGROUND: Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. RESULTS: In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI) data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. CONCLUSION: Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and chemical data sets.