Computational Exploration of Molecular Scaffolds in Medicinal Chemistry

Expanding the Repertoire of Large Scaffolds with Syn and Anti Macrocyclic Metacyclophanes

Understanding how changes in structure translate to changes in molecular shape is key to catalyst optimization and molecular design in medicinal chemistry and materials. One key contributor to the molecular shape is the relative orientation of substituents on a scaffold. Macrocyclic metacyclophanes display their two arenes in a parallel or antiparallel fashion, resulting in anti or syn conformations that lead to disparate relative orientations of the aryl substituents. This work reports the synthesis of new 14- and 16-membered metacyclophanes and the elucidation of their anti/syn preferences by 1H NMR and computational conformational analysis. Most metacyclophanes studied herein display a strong anti or syn preference and, thus, have well-defined substituent orientations. We propose that anti/syn conformational preferences arise from the minimization of torsional strain along the backbone of the macrocycle, which leads to the prediction that metacyclophanes with remote aryl substituents will adopt the same conformation as their unsubstituted counterparts. Exit vector analysis also reveals that anti-metacyclophanes project their substituents into regions in three-dimensional space that are not accessed by other common large scaffolds, e.g., [2.2]paracyclophanes and ferrocenes. This work also demonstrates how ring size and functional groups, two parameters commonly optimized in macrocycle design, can be used to tune molecular shape.

How to nurture natural products to create new therapeutics: Strategic innovations and molecule-to-medicinal insights into therapeutic advancements

Natural products (NPs) are privileged structures interacting with biomacromolecular targets and exhibiting biological effects important for human health. In this review, we have presented NP-inspired strategic innovations that are promising for addressing preclinical and clinical challenges. An analysis of 'molecule-to-medicinal' properties for improvement of P3 and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles has been illustrated. The strategies include chemical evolution through knowledge of structure-medicinal properties, truncation of NPs to avoid molecular obesity, pseudo-NPs, selection of common structural features of NPs, medicinophore installation, scaffold hopping, and induced proximity. Molecule-to-medicinal property analysis can guide the development of 'nature-to-new' chemical therapeutics. Coupled with scientific advances and innovations in instrumentation, these strategies hold great potential for enhancing drug design and discovery.

Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges

INTRODUCTION

Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead compound's pharmacodynamic, pharmacokinetic, and physiochemical properties. Scaffold hopping opens up fresh molecular territory beyond established patented chemical domains.

AREA COVERED

The authors present the scaffold hopping-based drug design strategies for dual inhibitory antitumor structural templates in this review. Minor modifications, structure rigidification and simplification (ring-closing and opening), and complete structural overhauls were the strategies employed by the medicinal chemist to generate a library of bifunctional inhibitors. In addition, the review presents an overview of the computational methods of scaffold hopping (software and programs) and organopalladium catalysis leveraged for the synthesis of templates designed via scaffold hopping.

EXPERT OPINION

The medicinal chemist has demonstrated remarkable prowess in furnishing dual inhibitory antitumor chemical architectures. Scaffold hopping-based drug design strategies have yielded a plethora of pharmacodynamically superior dual modulatory antitumor agents. An integrated approach involving computational advancements, synthetic methodology advancements, and conventional drug design strategies is required to increase the number of scaffold-hopping-assisted drug discovery campaigns.

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications

Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the 'Scaffold Identification and Naming System' (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

Targeting cancer using scaffold-hopping approaches: illuminating SAR to improve drug design

Scaffold hopping is a design approach involving alterations to the core structure of an already bioactive scaffold to generate novel molecules to discover bioactive hit compounds with innovative core structures. Scaffold hopping enhances selectivity and potency while maintaining physicochemical, pharmacodynamic (PD), and pharmacokinetic (PK) properties, including toxicity parameters. Numerous molecules have been designed based on a scaffold-hopping strategy that showed potent inhibition activity against multiple targets for the diverse types of malignancy. In this review, we critically discuss recent applications of scaffold hopping along with essential components of medicinal chemistry, such as structure-activity relationship (SAR) profiles. Moreover, we shed light on the limitations and challenges associated with scaffold hopping-based anticancer drug discovery.

Computational Screening Using a Combination of Ligand-Based Machine Learning and Molecular Docking Methods for the Repurposing of Antivirals Targeting the SARS-CoV-2 Main Protease

BACKGROUND

COVID-19 is an infectious disease caused by SARS-CoV-2, a close relative of SARS-CoV. Several studies have searched for COVID-19 therapies. The topics of these works ranged from vaccine discovery to natural products targeting the SARS-CoV-2 main protease (Mpro), a potential therapeutic target due to its essential role in replication and conserved sequences. However, published research on this target is limited, presenting an opportunity for drug discovery and development.

METHOD

This study aims to repurpose 10692 drugs in DrugBank by using ligand-based virtual screening (LBVS) machine learning (ML) with Konstanz Information Miner (KNIME) to seek potential therapeutics based on Mpro inhibitors. The top candidate compounds, the native ligand (GC-376) of the Mpro inhibitor, and the positive control boceprevir were then subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) characterization, drug-likeness prediction, and molecular docking (MD). Protein-protein interaction (PPI) network analysis was added to provide accurate information about the Mpro regulatory network.

RESULTS

This study identified 3,166 compound candidates inhibiting Mpro. The random forest (RF) molecular access system ML model provided the highest confidence score of 0.95 (bromo-7-nitroindazole) and identified the top 22 candidate compounds. Subjecting the 22 candidate compounds, the native ligand GC-376, and boceprevir to further ADMET property characterization and drug-likeness predictions revealed that one compound had two violations of Lipinski's rule. Additional MD results showed that only five compounds had more negative binding energies than the native ligand (- 12.25 kcal/mol). Among these compounds, CCX-140 exhibited the lowest score of - 13.64 kcal/mol. Through literature analysis, six compound classes with potential activity for Mpro were discovered. They included benzopyrazole, azole, pyrazolopyrimidine, carboxylic acids and derivatives, benzene and substituted derivatives, and diazine. Four pathologies were also discovered on the basis of the Mpro PPI network.

CONCLUSION

Results demonstrated the efficiency of LBVS combined with MD. This combined strategy provided positive evidence showing that the top screened drugs, including CCX-140, which had the lowest MD score, can be reasonably advanced to the in vitro phase. This combined method may accelerate the discovery of therapies for novel or orphan diseases from existing drugs.

LigandDiff: de Novo Ligand Design for 3D Transition Metal Complexes with Diffusion Models

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.

Effect of Poria cocos Terpenes: Verifying Modes of Action Using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network Analyses

We characterized the therapeutic biological modes of action of several terpenes in Poria cocos F.A Wolf (PC) and proposed a broad therapeutic mode of action for PC. Molecular docking and drug-induced transcriptome analysis were performed to confirm the pharmacological mechanism of PC terpene, and a new analysis method, namely diffusion network analysis, was proposed to verify the mechanism of action against Alzheimer's disease. We confirmed that the compound that exists only in PC has a unique mechanism through statistical-based docking analysis. Also, docking and transcriptomic analysis results could reflect results in clinical practice when used complementarily. The detailed pharmacological mechanism of PC was confirmed by constructing and analyzing the Alzheimer's disease diffusion network, and the antioxidant activity based on microglial cells was verified. In this study, we used two bioinformatics approaches to reveal PC's broad mode of action while also using diffusion networks to identify its detailed pharmacological mechanisms of action. The results of this study provide evidence that future pharmacological mechanism analysis should simultaneously consider complementary docking and transcriptomics and suggest diffusion network analysis, a new method to derive pharmacological mechanisms based on natural complex compounds.

Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors

This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.

Similarity searching for anticandidal agents employing a repurposing approach

Fungal infections caused by Candida are still a public health concern. Particularly, the resistance to traditional chemotherapeutic agents is a major issue that requires efforts to develop new therapies. One of the most interesting approaches to finding new active compounds is drug repurposing aided by computational methods. In this work, two databases containing anticandidal agents and drugs were studied employing cheminformatics and compared by similarity methods. The results showed 36 drugs with high similarities to some candicidals. From these drugs, trimetozin, osalmid and metochalcone were evaluated against C. albicans (18804), C. glabrata (90030), and miconazole-resistant strain C. glabrata (32554). Osalmid and metochalcone were the best, with activity in the micromolar range. These findings represent an opportunity to continue with the research on the potential antifungal application of osalmid and metochalcone as well as the design of structurally related derivatives.

Molecular medicinal insights into scaffold hopping-based drug discovery success

In both academia and the pharmaceutical industry, innovative hypotheses, methodologies and technologies that can shorten the drug research and development, leading to higher success rates, are vital. In this review, we demonstrate how innovative variations of the scaffold-hopping strategy have been used to create new druggable molecular spaces, drugs, clinical candidates, preclinical candidates, and bioactive agents. We also analyze molecular modulations that enabled improvements of the pharmacodynamic (PD), physiochemical, and pharmacokinetic (PK) properties (P3 properties) of the drugs resulting from these scaffold-hopping strategies.

MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction

Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.

Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations

Natural products have successfully treated several diseases using a multi-component, multi-target mechanism. However, a precise mechanism of action (MOA) has not been identified. Systems pharmacology methods have been used to overcome these challenges. However, there is a limitation as those similar mechanisms of similar components cannot be identified. In this study, comparisons of physicochemical descriptors, molecular docking analysis and RNA-seq analysis were performed to compare the MOA of similar compounds and to confirm the changes observed when similar compounds were mixed and used. Various analyses have confirmed that compounds with similar structures share similar MOA. We propose an advanced method for in silico experiments in herbal medicine research based on the results. Our study has three novel findings. First, an advanced network pharmacology research method was suggested by partially presenting a solution to the difficulty in identifying multi-component mechanisms. Second, a new natural product analysis method was proposed using large-scale molecular docking analysis. Finally, various biological data and analysis methods were used, such as in silico system pharmacology, docking analysis and drug response RNA-seq. The results of this study are meaningful in that they suggest an analysis strategy that can improve existing systems pharmacology research analysis methods by showing that natural product-derived compounds with the same scaffold have the same mechanism.

Excitation of NH Stretching Modes in Aromatic Molecules: o-Toluidine and α-Methylbenzylamine

Selectively excited o-toluidine and α-methylbenzylamine have been studied with quasi-classical trajectory procedures to determine the extent and timescales of intramolecular energy flow. The initial excitation is in the stretching mode of the para-CH bond, and its flow is initiated by interaction with an argon atom. Energy flow to the NH stretching mode is the dominant relaxation pathway, and its effectiveness is enhanced strongly by the methyl-NH interaction. Energy flow characteristics in both molecules are similar, but the flow is more effective in o-toluidine than in α-methylbenzylamine because the methyl group bonded to the benzene ring exerts stronger perturbation on the energy-flow pathway than the group bonded to the side chain. The relaxation of the initially excited CH completes on a timescale of several picoseconds, but the main part of energy flow to the NH occurs on a subpicosecond scale. In o-toluidine, carbon-carbon overtone modes lead to ring-CC bonds gaining and transporting more energy than high-frequency CH bonds, but they all gain far less energy than the NH stretching mode.

MBC and ECBL libraries: outstanding tools for drug discovery

Chemical libraries have become of utmost importance to boost drug discovery processes. It is widely accepted that the quality of a chemical library depends, among others, on its availability and chemical diversity which help in rising the chances of finding good hits. In this regard, our group has developed a source for useful chemicals named Medicinal and Biological Chemistry (MBC) library. It originates from more than 30 years of experience in drug design and discovery of our research group and has successfully provided effective hits for neurological, neurodegenerative and infectious diseases. Moreover, in the last years, the European research infrastructure for chemical biology EU-OPENSCREEN has generated the European Chemical Biology library (ECBL) to be used as a source of hits for drug discovery. Here we present and discuss the updated version of the MBC library (MBC v.2022), enriched with new scaffolds and containing more than 2,500 compounds together with ECBL that collects about 100,000 small molecules. To properly address the improved potentialities of the new version of our MBC library in drug discovery, up to 44 among physicochemical and pharmaceutical properties have been calculated and compared with those of other well-known publicly available libraries. For comparison, we have used ZINC20, DrugBank, ChEMBL library, ECBL and NuBBE along with an approved drug library. Final results allowed to confirm the competitive chemical space covered by MBC v.2022 and ECBL together with suitable drug-like properties. In all, we can affirm that these two libraries represent an interesting source of new hits for drug discovery.

Explaining compound activity predictions with a substructure-aware loss for graph neural networks

Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular feature attribution methods have so far displayed low performance for popular deep learning algorithms such as graph neural networks (GNNs), especially when compared with simpler modeling alternatives such as random forests coupled with atom masking. To mitigate this problem, a modification of the regression objective for GNNs is proposed to specifically account for common core structures between pairs of molecules. The presented approach shows higher accuracy on a recently-proposed explainability benchmark. This methodology has the potential to assist with model explainability in drug discovery pipelines, particularly in lead optimization efforts where specific chemical series are investigated.

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood-brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

Design, synthesis and cytotoxic evaluation of a selective serotonin reuptake inhibitor (SSRI) by virtual screening

Depression is one of the most common mental illnesses, affecting almost 300 million people. According to the WHO, depression is one of the world's leading causes of disability and morbidity. People with this illness require both psychological and pharmaceutical treatment because severe depressive episodes often result in suicide. Selective serotonin reuptake inhibitors (SSRI) are widely used antidepressants that target the human serotonin transporter (hSERT). The crystallization of hSERT and the experimental data available allows cost and time-efficient computational tools like virtual screening (VS) to be utilized in the development of therapeutic agents. Here, we synthesized, characterized, and evaluated the biological activity of a novel SSRI analog of paroxetine, rationally designed by applying an artificial neural network-based QSAR model and a molecular docking analysis on hSERT. The analog N-substituted 18a showed higher affinity for the transporter (-10.2 kcal/mol), lower Ki value (1.19 nM) and a safer toxicological profile than paroxetine and was synthesized with a 71% yield. The in vitro cytotoxicity of the analog was evaluated using human glioblastoma (U87 MG), human neuroblastoma (SH SY5Y) and murine fibroblast (L929) cell lines. Also, the hemolytic ability of the compound was assessed on human erythrocytes. Results showed that analog 18a did not exhibit cytotoxic activity on the cell lines used and has no hemolytic activity at any of the concentrations tested, whereas with paroxetine, hemolysis was observed at 2.3, 1.29 y 0.67 mM. Based on these results, it is possible to suggest that analog 18a could be a promising new SSRI candidate for the treatment of this illness.

Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules

Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.

One-Pot Sequence of Staudinger/aza-Wittig/Castagnoli-Cushman Reactions Provides Facile Access to Novel Natural-like Polycyclic Ring Systems

Realization of the one-pot Staudinger/aza-Wittig/Castagnoli-Cushman reaction sequence for a series of azido aldehydes and homophthalic anhydrides is described. The reaction proceeded at room temperature and delivered novel polyheterocycles related to the natural product realm in high yields and high diastereoselectivity. The methodology has been extended to three other cyclic anhydrides. These further unravel the potential of the Castagnoli-Cushman reaction in generating polyheterocyclic molecular scaffolds.

Design of a Structurally Novel Multipotent Drug Candidate by the Scaffold Architecture Technique for ACE-II, NSP15, and Mpro Protein Inhibition: Identification and Isolation of a Natural Product to Prevent the Severity of Future Variants of Covid 19 and a Colorectal Anticancer Drug

Scaffold architecture in the sectors of biotechnology and drug discovery research include scaffold hopping and molecular modelling techniques and helps in searching for potential drug candidates containing different core structures using computer-based software, which greatly aids medicinal and pharmaceutical chemistry. Going ahead, the computational method of scaffold architecture is thought to produce new scaffolds, and the method is capable of helping search engines toward producing new scaffolds that are likely to represent potent compounds with high therapeutic applications, which is a possibility in this case as well. Here we probate a different interactive design by natural product hopping, molecular modelling, pharmacophore modelling, modification, and combination of the phytoconstituents present in different medicinal plants for developing a pharmacophore-guided good drug candidate for the variants of SARS-CoV-2 or Covid 19. In the modern era, these approaches are carried out at every level of development of scaffold queries, which are increasingly summarized from chemical structures. In this context, we report on a successfully designed drug-like candidate having a high-binding-affinity "compound SLP" by understanding the relationships between the compounds' pharmacophores, scaffold functional groups, and biological activities beyond their individual applications that abide by Lipinski's rule of five, Ghose rule, Veber rule etc. The new scaffold generated by altering the core of the known phyto-compounds holds a good predicted ADMET profile and is examined with iMODS server to check the molecular dynamics simulation with normal mode analysis (NMA). The scaffold's three-dimensional (3D) structure yields a searchable natural product koenimbine from a conformer database having good ADMET property and high availability in spice Murraya koenigii leaves. M. koenigii leaves are easily available in the market, and might ensure the immunity, good health, and well-being of people if affected with any of the variants of Covid 19. The cell viability studies of koenimbine on murine colorectal carcinoma cell line (CT-26) showed no toxicity on normal mice lymphocyte cells (MLCs). The anticancer mechanism of koenimbine was displayed by its enhanced capacity to produce intercellular reactive oxygen species (ROS) in the colorectal carcinoma cell line.

Investigating novel thiazolyl-indazole derivatives as scaffolds for SARS-CoV-2 MPro inhibitors

COVID-19 is a global pandemic caused by infection with the SARS-CoV-2 virus. Remdesivir, a SARS-CoV-2 RNA polymerase inhibitor, is the only drug to have received widespread approval for treatment of COVID-19. The SARS-CoV-2 main protease enzyme (MPro), essential for viral replication and transcription, remains an active target in the search for new treatments. In this study, the ability of novel thiazolyl-indazole derivatives to inhibit MPro is evaluated. These compounds were synthesized via the heterocyclization of phenacyl bromide with (R)-carvone, (R)-pulegone and (R)-menthone thiosemicarbazones. The binding affinity and binding interactions of each compound were evaluated through Schrödinger Glide docking, AMBER molecular dynamics simulations, and MM-GBSA free energy estimation, and these results were compared with similar calculations of MPro binding various 5-mer substrates (VKLQA, VKLQS, VKLQG) and a previously identified MPro tight-binder X77. From these simulations, we can see that binding is driven by residue specific interactions such as π-stacking with His41, and S/π interactions with Met49 and Met165. The compounds were also experimentally evaluated in a MPro biochemical assay and the most potent compound containing a phenylthiazole moiety inhibited protease activity with an IC50 of 92.9 ​μM. This suggests that the phenylthiazole scaffold is a promising candidate for the development of future MPro inhibitors.

Development of a chemogenomics library for phenotypic screening

With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

Deep scaffold hopping with multimodal transformer neural networks

Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios.

Fragment-based lead discovery of indazole-based compounds as AXL kinase inhibitors

AXL is a member of the TAM (TYRO3, AXL, MER) subfamily of receptor tyrosine kinases. It is upregulated in a variety of cancers and its overexpression is associated with poor disease prognosis and acquired drug resistance. Utilizing a fragment-based lead discovery approach, a new indazole-based AXL inhibitor was obtained. The indazole fragment hit 11, identified through a high concentration biochemical screen, was expeditiously improved to fragment 24 by screening our in-house expanded library of fragments (ELF) collection. Subsequent fragment optimization guided by docking studies provided potent inhibitor 54 with moderate exposure levels in mice. X-ray crystal structure of analog 50 complexed with the I650M mutated kinase domain of Mer revealed the key binding interactions for the scaffold. The good potency coupled with reasonable kinase selectivity, moderate in vivo exposure levels, and availability of structural information for the series makes it a suitable starting point for further optimization efforts.

How different are marine microbial natural products compared to their terrestrial counterparts?

Covering: 1877 to 2020A key challenge in natural products research is the selection of biodiversity to yield novel chemistry. Recently, marine microorganisms have become a preferred source. But how novel are marine microorganism natural products compared to those reported from terrestrial microbes? Cluster analysis of chemical fingerprints and molecular scaffold analysis of 55 817 compounds reported from marine and terrestrial microorganisms, and marine macro-organisms showed that 76.7% of the compounds isolated from marine microorganisms are closely related to compounds isolated from terrestrial microorganisms. Only 14.3% of marine microorganism natural products are unique when marine macro-organism natural products are also considered. Studies targeting marine specific and understudied microbial phyla result in a higher likelihood of finding marine specific compounds, whereas the depth and geographic location of microorganism collection have little influence. We recommend marine targeted strain isolation, incorporating early use of genomic sequencing to guide strain selection, innovation in culture media and cultivation techniques and the application of cheminformatics tools to focus on unique natural product diversity, rather than the dereplication of known compounds.

Cyclic Dipeptides: The Biological and Structural Landscape with Special Focus on the Anti-Cancer Proline-Based Scaffold

Cyclic dipeptides, also know as diketopiperazines (DKP), the simplest cyclic forms of peptides widespread in nature, are unsurpassed in their structural and bio-functional diversity. DKPs, especially those containing proline, due to their unique features such as, inter alia, extra-rigid conformation, high resistance to enzyme degradation, increased cell permeability, and expandable ability to bind a diverse of targets with better affinity, have emerged in the last years as biologically pre-validated platforms for the drug discovery. Recent advances have revealed their enormous potential in the development of next-generation theranostics, smart delivery systems, and biomaterials. Here, we present an updated review on the biological and structural profile of these appealing biomolecules, with a particular emphasis on those with anticancer properties, since cancers are the main cause of death all over the world. Additionally, we provide a consideration on supramolecular structuring and synthons, based on the proline-based DKP privileged scaffold, for inspiration in the design of compound libraries in search of ideal ligands, innovative self-assembled nanomaterials, and bio-functional architectures.

"Molecular Anatomy": a new multi-dimensional hierarchical scaffold analysis tool

The scaffold representation is widely employed to classify bioactive compounds on the basis of common core structures or correlate compound classes with specific biological activities. In this paper, we present a novel approach called "Molecular Anatomy" as a flexible and unbiased molecular scaffold-based metrics to cluster large set of compounds. We introduce a set of nine molecular representations at different abstraction levels, combined with fragmentation rules, to define a multi-dimensional network of hierarchically interconnected molecular frameworks. We demonstrate that the introduction of a flexible scaffold definition and multiple pruning rules is an effective method to identify relevant chemical moieties. This approach allows to cluster together active molecules belonging to different molecular classes, capturing most of the structure activity information, in particular when libraries containing a huge number of singletons are analyzed. We also propose a procedure to derive a network visualization that allows a full graphical representation of compounds dataset, permitting an efficient navigation in the scaffold's space and significantly contributing to perform high quality SAR analysis. The protocol is freely available as a web interface at https://ma.exscalate.eu .

Discovery of an Unexpected Similarity in Ligand Binding between BRD4 and PPARγ

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. Here, we present a new concept that combines a scaffold-based analysis of bioactivity data with a subsequent screening to identify novel inhibitors for a protein target of interest. The initial scaffold-based analysis revealed a flavone-like scaffold that can be found in ligands of different unrelated proteins indicating a similarity in ligand binding. This similarity was further investigated by testing compounds on bromodomain-containing protein 4 (BRD4) that were similar to known ligands of the other identified protein targets. Several new BRD4 inhibitors were identified and proven to be validated hits based on orthogonal assays and X-ray crystallography. The most important discovery was an unexpected relationship between BRD4 and peroxisome-proliferator activated receptor gamma (PPARγ). Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such dual-BRD4-PPARγ modulators open up new therapeutic opportunities, because both are important drug targets for cancer therapy and many more important diseases. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.

R-group replacement database for medicinal chemistry

Aim:

Generation of an R-group replacement system for compound optimization in medicinal chemistry.

Materials & methods:

From bioactive compounds, analogue series (ASs) were systematically extracted and from these ASs, all R-groups were isolated and further analyzed.

Exemplary results & data:

From more than 17,000 ASs, more than 50,000 unique R-groups were isolated. For the 500 most frequently used R-groups, preferred replacements were identified and organized in hierarchies. All original data and an R-group replacement database are made available in an open access deposition.

Limitations & next steps:

The searchable database has no limitations and can easily be modified using the source data we provide. The next step will be applying this R-group resource in practical medicinal chemistry projects as decision support.

To optimize the biological activity of small molecules in medicinal chemistry, series of analogues are generated by introducing substituents (R-groups) at different positions. The choice of R-groups largely depends on the experience of individual chemists. We have computationally isolated a large number of R-groups from currently available analogue series. Frequently used R-groups and their preferred replacements were identified and organized in a searchable database for medicinal chemists to aid in R-group selection.

Cyclic Dipeptide: A Privileged Molecular Scaffold to Derive Structural Diversity and Functional Utility

Cyclic dipeptides (CDPs) are the simplest form of cyclic peptides with a wide range of applications from therapeutics to biomaterials. CDP is a versatile molecular platform endowed with unique properties such as conformational rigidity, intermolecular interactions, structural diversification through chemical synthesis, bioavailability and biocompatibility. A variety of natural products with the CDP core exhibit anticancer, antifungal, antibacterial, and antiviral activities. The inherent bioactivities have inspired the development of synthetic analogues as drug candidates and drug delivery systems. CDP plays a crucial role as conformation and molecular assembly directing core in the design of molecular receptors, peptidomimetics and fabrication of functional material architectures. In recent years, CDP has rapidly become a privileged scaffold for the design of advanced drug candidates, drug delivery agents, bioimaging, and biomaterials to mitigate numerous disease conditions. This review describes the structural diversification and multifarious biomedical applications of the CDP scaffold, discusses challenges, and provides future directions for the emerging field.

An effective self-supervised framework for learning expressive molecular global representations to drug discovery

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.

Medicinal chemistry of P2 and adenosine receptors: Common scaffolds adapted for multiple targets

Prof. Geoffrey Burnstock originated the concept of purinergic signaling. He demonstrated the interactions and biological roles of ionotropic P2X and metabotropic P2Y receptors. This review paper traces the historical origins of many currently used antagonists and agonists for P2 receptors, as well as adenosine receptors, in early attempts to identify ligands for these receptors - prior to the use of chemical libraries for screening. Rather than presenting a general review of current purinergic ligands, we focus on common chemical scaffolds (privileged scaffolds) that can be adapted for multiple receptor targets. By carefully analyzing the structure activity relationships, one can direct the selectivity of these scaffolds toward different receptor subtypes. For example, the weak and non-selective P2 antagonist reactive blue 2 (RB-2) was derivatized using combinatorial synthetic approaches, leading to the identification of selective P2Y2, P2Y4, P2Y12 or P2X2 receptor antagonists. A P2X4 antagonist NC-2600 is in a clinical trial, and A3 adenosine agonists show promise, for chronic pain. P2X7 antagonists have been in clinical trials for depression (JNJ-54175446), inflammatory bowel disease (IBD), Crohn's disease, rheumatoid arthritis, inflammatory pain and chronic obstructive pulmonary disease (COPD). P2X3 antagonists are in clinical trials for chronic cough, and an antagonist named after Burnstock, gefapixant, is expected to be the first P2X3 antagonist filed for approval. We are seeing that the vision of Prof. Burnstock to use purinergic signaling modulators, most recently at P2XRs, for treating disease is coming to fruition.

Ubiquitin-proteasome system and the role of its inhibitors in cancer therapy

Despite all the other cells that have the potential to prevent cancer development and metastasis through tumour suppressor proteins, cancer cells can upregulate the ubiquitin–proteasome system (UPS) by which they can degrade tumour suppressor proteins and avoid apoptosis. This system plays an extensive role in cell regulation organized in two steps. Each step has an important role in controlling cancer. This demonstrates the importance of understanding UPS inhibitors and improving these inhibitors to foster a new hope in cancer therapy. UPS inhibitors, as less invasive chemotherapy drugs, are increasingly used to alleviate symptoms of various cancers in malignant states. Despite their success in reducing the development of cancer with the lowest side effects, thus far, an appropriate inhibitor that can effectively inactivate this system with the least drug resistance has not yet been fully investigated. A fundamental understanding of the system is necessary to fully elucidate its role in causing/controlling cancer. In this review, we first comprehensively investigate this system, and then each step containing ubiquitination and protein degradation as well as their inhibitors are discussed. Ultimately, its advantages and disadvantages and some perspectives for improving the efficiency of these inhibitors are discussed.

Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering

In their previous work, Srinivas et al. [ J. Cheminf. 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known bioactivity data. In this work, we extend these implicit fingerprints/descriptors using deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties. This allows the design of new compounds based upon the latent representation of nearby proteins, thereby encoding druglike properties including binding affinities to known proteins. The implicit descriptor method does not require any fingerprint similarity search, which makes the method free of any bias arising from the empirical nature of the fingerprint models [Srinivas, R.; J. Cheminf. 2018, 10, 56]. We evaluate the properties of the potentially novel drugs generated by our approach using physical properties of druglike molecules and chemical complexity. Additionally, we analyze the reliability of the biological activity of the new compounds generated using this method by employing models of protein-ligand interaction, which assists in assessing the potential binding affinity of the designed compounds. We find that the generated compounds exhibit properties of chemically feasible compounds and are predicted to be excellent binders to known proteins. Furthermore, we also analyze the diversity of compounds created using the Tanimoto distance and conclude that there is a wide diversity in the generated compounds.

Recent investigations into synthesis and pharmacological activities of phenoxy acetamide and its derivatives (chalcone, indole and quinoline) as possible therapeutic candidates

Medicinal chemistry can rightfully be regarded as a cornerstone in the public health of our modern society that combines chemistry and pharmacology with the aim of designing and developing new pharmaceutical compounds. For this purpose, many chemical techniques as well as new computational chemistry applications are used to study the utilization of drugs and their biological effects. In the biological interface, medicinal chemistry constitutes a group of interdisciplinary sciences, as well as controlling its organic, physical and computational pillars. Therefore, medicinal chemists working to design an integrated and developing system that portends an era of novel and safe tailored drugs either by synthesizing new pharmaceuticals or to improving the processes by which existing pharmaceuticals are made. It includes researching the effects of synthetic, semi-synthetic and natural biologically active substances based on molecular interactions in terms of molecular structure with triggered functional groups or the specific physicochemical properties. The present work focuses on the literature survey of chemical diversity of phenoxy acetamide and its derivatives (Chalcone, Indole and Quinoline) in the molecular framework in order to get complete information regarding pharmacologically interesting compounds of widely different composition. From a biological and industrial point of view, this literature review may provide an opportunity for the chemists to design new derivatives of phenoxy acetamide and its derivatives that proved to be the successful agent in view of safety and efficacy to enhance life quality.

AIScaffold: A Web-Based Tool for Scaffold Diversification Using Deep Learning

Molecular scaffolds are widely used in drug design. Many methods and tools have been developed to utilize the information in scaffolds. Scaffold diversification is frequently used by medicinal chemists in tasks such as lead compound optimization, but tools for scaffold diversification are still lacking. Here, we propose AIScaffold (https://iaidrug.stonewise.cn), a web-based tool for scaffold diversification using the deep generative model. This tool can perform large-scale (up to 500,000 molecules) diversification in several minutes and recommend the top 500 (top 0.1%) molecules. Features such as site-specific diversification are also supported. This tool can facilitate the scaffold diversification process for medicinal chemists, thereby accelerating drug design.

ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees

SUMMARY

ScaffoldGraph (SG) is an open-source Python library and command-line tool for the generation and analysis of molecular scaffold networks and trees, with the capability of processing large sets of input molecules. With the increase in high-throughput screening data, scaffold graphs have proven useful for the navigation and analysis of chemical space, being used for visualization, clustering, scaffold-diversity analysis and active-series identification. Built on RDKit and NetworkX, SG integrates scaffold graph analysis into the growing scientific/cheminformatics Python stack, increasing the flexibility and extendibility of the tool compared to existing software.

AVAILABILITY AND IMPLEMENTATION

SG is freely available and released under the MIT licence at https://github.com/UCLCheminformatics/ScaffoldGraph.

Chemography: Searching for Hidden Treasures

The days when medicinal chemistry was limited to a few series of compounds of therapeutic interest are long gone. Nowadays, no human may succeed to acquire a complete overview of more than a billion existing or feasible compounds within which the potential "blockbuster drugs" are well hidden and yet only a few mouse clicks away. To reach these "hidden treasures", we adapted the generative topographic mapping method to enable efficient navigation through the chemical space, from a global overview to a structural pattern detection, covering, for the first time, the complete ZINC library of purchasable compounds, relative to 1.6 million biologically relevant ChEMBL molecules. About 40 000 hierarchical maps of the chemical space were constructed. Structural motifs inherent to only one library were identified. Roughly 20 000 off-market ChEMBL compound families represent incentives to enrich commercial catalogs. Alternatively, 125 000 ZINC-specific compound classes, absent in structure-activity bases, are novel paths to explore in medicinal chemistry. The complete list of these chemotypes can be downloaded using the link https://forms.gle/B6bUJj82t9EfmttV6.

Molecular Diversity via Tetrasubstituted Alkenes Containing a Barbiturate Motif: Synthesis and Biological Activity

The synthesis of a molecularly diverse library of tetrasubstituted alkenes containing a barbiturate motif is described. Base-induced condensation of N¹-substituted pyrimidine-2,4,6(1H,3H,5H)-triones with 5-(bis(methylthio)methylene)-2,2-dimethyl-1,3-dioxane-4,6-dione gave 3-substituted 5-(methylthio)-2H-pyrano[2,3-d]pyrimidine-2,4,7(1H,3H)-triones (‘pyranopyrimidinones’), regioselectively. A sequence of reactions involving ring-opening of the pyran moiety, displacement of the methylthio group with an amine, re-formation of the pyran ring, and after its final cleavage with an amine, gave tetrasubstituted alkenes (3-amino-3-(2,4,6-trioxotetrahydropyrimidin-5(2H)-ylidene)propanamides) with a diversity of substituents. Cleavage of the pyranopyrimidinones with an aniline was facilitated in 2,2,2-trifluoroethanol under microwave irradiation. Compounds were tested against Escherichia coli, Staphylococcus aureus, the yeast Schizosaccharomyces pombe, and the pathogenic fungus Candida albicans. No compounds exhibited activity against E. coli, whilst one compound was weakly active against S. aureus. Three compounds were strongly active against S. pombe, but none was active against C. albicans.

Azolo[d]pyridazinones in medicinal chemistry

Azolo[d]pyridazinone is a privileged structure and versatile pharmacophore whose derivatives are associated with diverse biological activities, in particular antidiabetic, antiasthmatic, anticancer, analgesic, anti-inflammatory, antithrombotic, antidepressant and antimicrobial activities. The importance of this scaffold against some targets like PDE, COX and DPP-4 has been reviewed in detail previously. In the present review, we have summarized comprehensive information on azolo[d]pyridazinone derivatives investigated by many researchers for their diverse pharmacological activities, structure-activity relationship and molecular modeling studies since 2000. The review may lead scientists in the research fields of organic synthesis, medicinal chemistry and pharmacology to the strategic design and development of azolo[d]pyridazinone-based drug candidates in the future.

Global Assessment of Substituents on the Basis of Analogue Series

While bioisosteric replacements have been extensively investigated, comprehensive analyses of R-/functional groups have thus far been rare in medicinal chemistry. We introduce a new analysis concept for the exploration of chemical substituent space that is based upon bioactive analogue series as a source. From ∼24,000 analogue series, more than 19,000 substituents were isolated that were differently distributed. A subset of ∼400 substituent fragments occurred most frequently in different structural contexts. These substituents contained well-known R-groups as well as novel structures. Substitution site-specific replacement and network analysis revealed that chemically similar substituents preferentially occurred at given sites and identified intuitive substitution pathways that can be explored for compound design. Taken together, the results of our analysis provide new insights into substituent space and identify preferred substituents on the basis of analogue series. As a part of our study, all the data reported are made freely available.

Structural Approach to Assessing the Innovativeness of New Drugs Finds Accelerating Rate of Innovation

Measuring innovation in the pharmaceutical industry is challenging. Counts of new molecular entities (NMEs) approved by the Food and Drug Administration (FDA) are commonly used, but this measure only gauges quantity not innovativeness. A new indicator of innovation for small molecule and peptide drugs based on structural novelty is proposed and used to analyze recent trends in pharmaceutical innovation. We show pharmaceutical innovation has significantly increased over the last several decades despite recent concerns over an innovation crisis and find Pioneers (a NME whose shape and scaffold were not used in any previously FDA-approved drugs) are significantly more likely to be the source of promising new therapies. Analysis of the underlying source of structural innovation indicates that scaffolds first reported in the CAS REGISTRY five or less years prior to their Investigational New Drug application (IND) or on scaffolds populated with 50 or less other compounds at the time of IND tend to be the main source of Pioneers. Our analysis also shows a widening structural innovation gap between large pharmaceutical companies (Big Pharma) and the rest of the ecosystem even though the number of Big Pharma originated Pioneers has increased.

Pharmacoinformatic Investigation of Medicinal Plants from East Africa

Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing ∼6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

rdScaffoldNetwork: The Scaffold Network Implementation in RDKit

We present an implementation of the scaffold network in the open source cheminformatics toolkit RDKit. Scaffold networks have been introduced in the literature as a powerful method to navigate and analyze large screening data sets in medicinal chemistry. Such a network can be created by iteratively applying predefined fragmentation rules to the investigated set of small molecules and by linking the produced fragments according to their descendence. This procedure results in a network graph, where the nodes correspond to the fragments and the edges correspond to the operations producing one fragment from another. In extension to the scaffold network implementations suggested in the literature, the presented implementation in RDKit allows an enhanced flexibility in terms of customizing the fragmentation rules and enables the inclusion of atom- and bond-generic scaffolds into the network. The output, providing node and edge information on the network, enables a simple and elegant navigation through the network, laying the basis to organize and better understand the data set being investigated.

Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors

Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.

Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics

Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.