Increasing chemical space coverage by combining empirical and computational fragment screens

DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression

The design of biosequences for biosensing and therapeutics is a challenging multistep search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs. Here, we describe a computational approach called DeltaGzip that evaluates the free energy of binding in biopolymer-ligand complexes from ultrashort equilibrium molecular dynamics simulations. The entropy of binding is evaluated using the Kolmogorov complexity definition of entropy and approximated using a lossless compression algorithm, Gzip. We benchmark the method on a well-studied data set of protein-ligand complexes comparing the predictions of DeltaGzip to the free energies of binding obtained using Jarzynski equality and experimental measurements.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of in vitro proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the in silico prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predict nine proxy-DILI labels and then use them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILI data set (composed of DILIst and DILIrank) and tested them on a held-out external test set of 223 compounds from the DILI data set. The best model, DILIPredictor, attained an AUC-PR of 0.79. This model enabled the detection of the top 25 toxic compounds (2.68 LR+, positive likelihood ratio) compared to models using only structural features (1.65 LR+ score). Using feature interpretation from DILIPredictor, we identified the chemical substructures causing DILI and differentiated cases of DILI caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as nontoxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity and the potential for mechanism evaluation. DILIPredictor required only chemical structures as input for prediction and is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download.

Expanding chemistry through in vitro and in vivo biocatalysis

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted in vivo and in vitro Data

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects. In this study, we aim to study ML models for DILI prediction that first predicts nine proxy-DILI labels and then uses them as features in addition to chemical structural features to predict DILI. The features include in vitro (e.g., mitochondrial toxicity, bile salt export pump inhibition) data, in vivo (e.g., preclinical rat hepatotoxicity studies) data, pharmacokinetic parameters of maximum concentration, structural fingerprints, and physicochemical parameters. We trained DILI-prediction models on 888 compounds from the DILIst dataset and tested on a held-out external test set of 223 compounds from DILIst dataset. The best model, DILIPredictor, attained an AUC-ROC of 0.79. This model enabled the detection of top 25 toxic compounds compared to models using only structural features (2.68 LR+ score). Using feature interpretation from DILIPredictor, we were able to identify the chemical substructures causing DILI as well as differentiate cases DILI is caused by compounds in animals but not in humans. For example, DILIPredictor correctly recognized 2-butoxyethanol as non-toxic in humans despite its hepatotoxicity in mice models. Overall, the DILIPredictor model improves the detection of compounds causing DILI with an improved differentiation between animal and human sensitivity as well as the potential for mechanism evaluation. DILIPredictor is publicly available at https://broad.io/DILIPredictor for use via web interface and with all code available for download and local implementation via https://pypi.org/project/dilipred/.

Carbon sphere doped CdS quantum dots served as a dye degrader and their bactericidal behavior analysed with in silico molecular docking analysis

We have employed a co-precipitation method to synthesize different concentrations of carbon spheres (CSs) doped with cadmium sulfide (CdS) quantum dots (QDs) for catalytic reduction and antibacterial applications. Various morphological and structural characterization techniques were used to comprehensively analyze the CS effect on CdS QDs. The catalytic reduction efficiency of CS-doped CdS QDs was evaluated using rhodamine B dye. The antibacterial efficacy was also assessed against the pathogenic microorganism Escherichia coli (E. coli), and substantial destruction in the inhibitory zone was measured. Finally, the synthesized CS-doped CdS QDs demonstrated favorable results for catalytic reduction and antibacterial applications. Computational studies verified the suppressive impact of these formed QDs on DNA gyrase and β-lactamase of E. coli.

Carbon Dots from Tire Waste for the Photodegradation of Methyl Orange Dye, Antimicrobial Activity, and Molecular Docking Study

In this study, solvothermal pathway was employed for the synthesis of P, N codoped C-dot using tire waste as a sustainable source of carbon and nitrogen. Comprehensive analyses encompassing X-ray diffraction (XRD) analysis, Transmission Electron Microscopy (TEM), FT-IR, cyclic voltammetry, and UV-Vis spectra were used to assess the crystalline structure, purity, size, fluorescence up-conversion, and morphological attributes of the nanomaterial. Subsequently, the produced C-dots were evaluated for their efficacy in the photocatalytic degradation of methylene blue and methyl orange dyes, demonstrating notable success in degrading methyl orange dye within eight hours in the visible region. Furthermore, the same nanomaterial was applied for carrying out agar disk-diffusion assays against a spectrum of microorganisms. Results revealed substantial inhibition zones against Methicillin-Resistant Staphylococcus aureus (MRSA), Escherichia coli, and Pseudomonas aeruginosa. Elucidating the antimicrobial mechanism, molecular-docking simulations were excuted using on AutoDock Vina with designated ligands. The results indicated a strong binding affinity of the C-dots with certain proteins associated with antibacterial activity. This observation suggests that the synthesized C-dots effectively engage with the active sites of these proteins, indicating their potential as promising antibacterial agents. Importantly, this study implies that C-dots do not induce protein denaturation, thereby warranting further investigation of their utility as antibacterial agents.

Antimicrobial potential and rhodamine B dye degradation using graphitic carbon nitride and polyvinylpyrrolidone doped bismuth tungstate supported with in silico molecular docking studies

The environmental-friendly hydrothermal method has been carried out to synthesize Bi2WO6 and g-C3N4/PVP doped Bi2WO6 nanorods (NRs) by incorporating different concentrations of graphitic carbon nitride (g-C3N4) as well as a specified quantity of polyvinylpyrrolidone (PVP). Bi2WO6 doped with g-C3N4 provides structural and chemical stability, reduces charge carriers, degrades dyes, and, owing to lower bandgap energy, is effective for antibacterial, catalytic activity, and molecular docking analysis. The purpose of this research is the treatment of polluted water and to investigate the bactericidal behavior of a ternary system. The catalytic degradation was performed to remove the harmful rhodamine B (RhB) dye using NaBH4 in conjunction with prepared NRs. The specimen compound demonstrated antibacterial activity against Escherichia coli (E. coli) at both high and low concentrations. Higher doped specimens of g-C3N4/PVP-doped Bi2WO6 exhibited a significant improvement in efficient bactericidal potential against E. coli (4.55 mm inhibition zone). In silico experiments were carried out on enoyl-[acylcarrier-protein] reductase (FabI) and β-lactamase enzyme for E. coli to assess the potential of Bi2WO6, PVP doped Bi2WO6, and g-C3N4/PVP-doped Bi2WO6 NRs as their inhibitors and to justify their possible mechanism of action.

Catalytic performance and antibacterial behaviour with molecular docking analysis of silver and polyacrylic acid doped graphene quantum dots

In this research, a fixed concentration (3 wt%) of Ag/PAA and PAA/Ag doped graphene quantum dots (GQDs) were synthesized using the co-precipitation technique. A variety of characterization techniques were employed to synthesize samples to investigate their optical, morphological, structural, and compositional analyses, antimicrobial efficacy, and dye degradation potential with molecular docking analysis. GQDs have high solubility, narrow band gaps, and are suitable for electron acceptors and donors but show less adsorption and catalytic behavior. Incorporating polyacrylic acid (PAA) into GQDs increases the catalytic and antibacterial activities due to the carboxylic group (-COOH). Furthermore, introducing silver (Ag) increased the degradation of dye and microbes as it had a high surface-to-volume ratio. In addition, molecular docking studies were used to decipher the mechanism underlying the bactericidal action of silver and polyacrylic acid-doped graphene quantum dots and revealed inhibition of β-lactamase and DNA gyrase.

Catalytic evaluation and in vitro bacterial inactivation of graphitic carbon nitride/carbon sphere doped bismuth oxide quantum dots with evidential in silico analysis

Herein, Bi2O3 quantum dots (QDs) have been synthesized and doped with various concentrations of graphitic carbon nitride (g-C3N4) and a fixed amount of carbon spheres (CS) using a co-precipitation technique. XRD analysis confirmed the presence of monoclinic structure along the space group P21/c and C2/c. Various functional groups and characteristic peaks of (Bi-O) were identified using FTIR spectra. QDs morphology of Bi2O3 showed agglomeration with higher amounts of g-C3N4 by TEM analysis. HR-TEM determined the variation in the d-spacing which increased with increasing dopants. These doping agents were employed to reduce the exciting recombination rate of Bi2O3 QDs by providing more active sites which enhance antibacterial activity. Notably, (6 wt%) g-C3N4/CS-doped Bi2O3 exhibited considerable antimicrobial potential in opposition to E. coli at higher values of concentrations relative to ciprofloxacin. The (3 wt%) g-C3N4/CS-doped Bi2O3 exhibits the highest catalytic potential (97.67%) against RhB in a neutral medium. The compound g-C3N4/CS-Bi2O3 has been suggested as a potential inhibitor of β-lactamaseE. coli and DNA gyraseE. coli based on the findings of a molecular docking study that was in better agreement with in vitro bactericidal activity.

Structure-Based Discovery of Inhibitors of the SARS-CoV-2 Nsp14 N7-Methyltransferase

An under-explored target for SARS-CoV-2 is the S-adenosyl methionine (SAM)-dependent methyltransferase Nsp14, which methylates the N7-guanosine of viral RNA at the 5'-end, allowing the virus to evade host immune response. We sought new Nsp14 inhibitors with three large library docking strategies. First, up to 1.1 billion lead-like molecules were docked against the enzyme's SAM site, leading to three inhibitors with IC50 values from 6 to 50 μM. Second, docking a library of 16 million fragments revealed 9 new inhibitors with IC50 values from 12 to 341 μM. Third, docking a library of 25 million electrophiles to covalently modify Cys387 revealed 7 inhibitors with IC50 values from 3.5 to 39 μM. Overall, 32 inhibitors encompassing 11 chemotypes had IC50 values < 50 μM and 5 inhibitors in 4 chemotypes had IC50 values < 10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing starting points for future optimization.

La-Doped CeO2 Quantum Dots: Novel Dye Degrader, Antibacterial Activity, and In Silico Molecular Docking Analysis

The current work demonstrates a novel synthesis of different concentrations of La-doped (2, 4, and 6 wt %) CeO₂ quantum dots (QDs) using a hydrothermal approach. This research aimed to examine the dye degradation efficiency, antibacterial activity, and in silico molecular docking analysis of La-doped CeO₂ QDs. The structure, elemental composition, optical properties, d-spacing, and morphological features of QDs were examined using various methods. XRD spectra exhibited the cubic structure of CeO₂, and the crystallinity was suppressed upon La doping. TEM revealed the formation of cubic-shaped QDs of CeO₂, and the incorporation of La decreased agglomeration. UV-vis absorption spectra showed a red shift upon La doping, assigned to a decrease in band gap energy. 6% La-doped CeO₂ showed significant antibacterial activity against Escherichia coli at higher concentrations in comparison to ciprofloxacin. La-CeO₂ was proposed as a putative inhibitor of β-lactamase _E. coli and DNA gyrase _E. coli relying on the outcomes of a molecular docking analysis that was in improved accord with in vitro bactericidal activity. Moreover, the prepared QDs exhibited a remarkable photocatalytic degradation of methylene blue in a basic medium.

Assessment of catalytic, antimicrobial and molecular docking analysis of starch-grafted polyacrylic acid doped BaO nanostructures

The removal of cationic dyes from water has received a great attention of researchers considering their influence on environment and ecosystem. In current work, starch-grafted-poly acrylic acid (St-g-PAA) doped BaO nanostrucutures have been synthesized by co-precipitation approach. The aim of this research was to reduce the harmful methylene blue dye and evaluate the antibacterial activity of St-g-PAA doped BaO. XRD spectra exhibited the tetragonal structure of BaO and no variations occurred upon doping. The optical properties of St-g-PAA doped BaO have been evaluated by UV-Vis spectrophotometer. The existence of a dopant in the product was verified using EDS spectroscopy. TEM revealed the formation of cubic-shaped NPs of BaO and upon the addition of St-g-PAA, a few nanorod-like structures. The higher concentration of St-g-PAA doped BaO exhibit a remarkable reduction of methylene blue in a basic environment. Furthermore, St-g-PAA doped BaO revealed higher antimicrobial efficacy against Staphylococcus aureus in comparison to Escherichia coli. In silico studies were conducted against enoyl-[acylcarrier-protein] reductase (FabI) and beta-lactamase enzyme to evaluate the potential of both St-g-PAA and St-g-PAA doped BaO nanocomposites as their inhibitors and to rationalize their possible mode of action.

Bactericidal Action and Industrial Dye Degradation of Graphene Oxide and Polyacrylic Acid-Doped SnO2 Quantum Dots: In Silico Molecular Docking Study

The present work demonstrates the systematic incorporation of different concentrations of graphene oxide (GO) into a fixed amount of polyacrylic acid (PAA)-doped SnO₂ quantum dots (QDs) through a co-precipitation approach. The research aimed to evaluate the catalytic and antibacterial actions of GO/PAA-SnO₂ QDs. Moreover, optical properties, surface morphologies, crystal structures, elemental compositions, and d-spacings of prepared QDs were examined. X-ray diffraction patterns revealed the tetragonal configuration of SnO₂, and the crystallinity of QDs was suppressed upon dopants verified by the SAED patterns. Electronic spectra identified the blue shift by incorporating GO and PAA led to a reduction in band gap energy. Fourier transform infrared spectra showed the existence of rotational and vibrational modes associated with the functional groups during the synthesis process. A drastic increase in the catalytic efficacy of QDs was observed in the neutral medium by including dopants, indicating that GO/PAA-SnO₂ is a promising catalyst. GO/PAA-SnO₂ showed strong bactericidal efficacy against Escherichia coli (E. coli) at higher GO concentrations. Molecular docking studies predicted the given nanocomposites, i.e., SnO₂, PAA-SnO₂, and GO/PAA-SnO₂, as potential inhibitors of beta-lactamase_E. coli and DNA gyrase_E. coli.

A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted 'good solvents' with an accuracy >0.8. Overall, we confirmed the effectivity of the proposed semi-automated scheme in early-stage material search projects for accelerating a wider range of material research.

Facile synthesis of starch and tellurium doped SrO nanocomposite for catalytic and antibacterial potential: In silico molecular docking studies

A chemical co-precipitation route was used to synthesize novel strontium oxide (SrO), SrO-starch composite and various tellurium (Te) concentrations were incorporated in SrO-starch composite. This study aims to enhance the catalytic activities and bactericidal behavior of SrO, SrO-starch composite with different percentage concentrations of Te doping and a fixed amount of starch nanoparticles. XRD affirmed that the dopant contribution was investigated to improve crystallinity. Surface morphological characteristics and elemental composition evaluation were determined using an FE-SEM and EDS exhibit a doping concentration of an element in the synthesized products. The configuration of Sr-O-Sr bonds and molecular vibrations has been indicated by FTIR spectra. In addition, dye degradation of prepared samples was investigated through catalytic activity (CA) in the existence of NaBH₄ act as a reduction representative. The Te-doped SrO-starch composite indicates superior catalytic activity and shows a degradation of Methylene blue dye (91.4 %) in an acidic medium. The synthesis nanocatalyst demonstrated impressive antibacterial activity against Staphylococcus aureus (S. aureus) at high and low concentrations exhibiting zones of inhibition 9.30 mm as compared to ciprofloxacin. Furthermore, molecular docking studies of synthesized nanocomposites were performed against selected enzyme targets, i.e., β-lactamase_E.coli and DNA Gyrase_E.coli.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes

To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

Class C β-Lactamases: Molecular Characteristics

Class C β-lactamases or cephalosporinases can be classified into two functional groups (1, 1e) with considerable molecular variability (≤20% sequence identity). These enzymes are mostly encoded by chromosomal and inducible genes and are widespread among bacteria, including Proteobacteria in particular. Molecular identification is based principally on three catalytic motifs (⁶⁴SXSK, ¹⁵⁰YXN, ³¹⁵KTG), but more than 70 conserved amino-acid residues (≥90%) have been identified, many close to these catalytic motifs. Nevertheless, the identification of a tiny, phylogenetically distant cluster (including enzymes from the genera Legionella, Bradyrhizobium, and Parachlamydia) has raised questions about the possible existence of a C2 subclass of β-lactamases, previously identified as serine hydrolases. In a context of the clinical emergence of extended-spectrum AmpC β-lactamases (ESACs), the genetic modifications observed in vivo and in vitro (point mutations, insertions, or deletions) during the evolution of these enzymes have mostly involved the Ω- and H-10/R2-loops, which vary considerably between genera, and, in some cases, the conserved triplet ¹⁵⁰YXN. Furthermore, the conserved deletion of several amino-acid residues in opportunistic pathogenic species of Acinetobacter, such as A. baumannii, A. calcoaceticus, A. pittii and A. nosocomialis (deletion of residues 304-306), and in Hafnia alvei and H. paralvei (deletion of residues 289-290), provides support for the notion of natural ESACs. The emergence of higher levels of resistance to β-lactams, including carbapenems, and to inhibitors such as avibactam is a reality, as the enzymes responsible are subject to complex regulation encompassing several other genes (ampR, ampD, ampG, etc.). Combinations of resistance mechanisms may therefore be at work, including overproduction or change in permeability, with the loss of porins and/or activation of efflux systems.

Iron-Zinc Co-Doped Titania Nanocomposite: Photocatalytic and Photobiocidal Potential in Combination with Molecular Docking Studies

In the current research study, iron-zinc co-doped TiO2 was reported as an energy efficient material for the degradation of DIPA and inactivation of E. coli and S. aureus under visible light irradiation. In addition, molecular docking simulation was performed to provide further insight into possible targets for inhibiting bacterial development. The synthesized nanocomposites were screened and optimized for different synthesis and reaction parameters. The physicochemical properties of the synthesized nanocomposites were evaluated through different characterization techniques. The wet impregnation (WI) approach was among the most successful methods for the synthesis of Fe-Zn-TiO2 nanocomposite (NC) utilizing anatase titanium. Moreover, 66.5% (60 min reaction time) and 100% (190 min reaction time) chemical oxygen demand (COD) removal was obtained through optimized NC, i.e., 0.1Fe-0.4Zn metal composition and 300 °C calcination temperature. The energy consumption for the best NC was 457.40 KW h m−3. Moreover, 0.1Fe-0.4Zn-TiO2-300 was more efficient against S. aureus compared to E. coli with 100% reduction in 90 min of visible light irradiations. Furthermore, 0.1Fe-0.4Zn-TiO2-300 NC showed that the binding score for best docked conformation was −5.72 kcal mol−1 against β-lactamase from E. coli and −3.46 kcal mol−1 from S. aureus. The studies suggested the Fe-Zn in combination with TiO2 to be a possible inhibitor of β-lactamase that can be further tested in enzyme inhibition studies.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Photocatalytic, Bactericidal and Molecular Docking Analysis of Annealed Tin Oxide Nanostructures

Nanosized tin oxide was fabricated with a simple and cost-effective precipitation technique and was analyzed by performing x-ray powder diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, high-resolution transmission electron (HR-TEM) microscopy, energy-dispersive x-ray (EDX) and UV-Vis spectroscopy. The XRD results revealed that tin oxide particles possessed typical orthorhombic structure and exhibited improved crystallinity with annealing. Calcination at 250 °C produced predominantly orthorhombic SnO which transformed to SnO₂ at higher temperatures of 500 and 750 °C. HRTEM and FESEM images showed existence of agglomeration within the particles of tin oxide. The absorption was found to increase up to a certain annealing temperature followed by a decrease, which was recorded via UV-Vis spectroscopy. The effect of annealing temperature on dye decomposition behavior of synthesized photocatalysts was studied. It was noted that annealing temperature affects the size of synthesized particles, band gap width and photoactivity of tin oxide. The sample prepared at 500 °C followed first-order kinetics and exhibited maximum photocatalytic reactivity toward methylene blue. The experimental results obtained from the present study indicate that SnO₂ is a promising and beneficial catalyst to remove contaminants from wastewater and environment. The antimicrobial evaluation of SnO annealed at 500 °C against selected targets such as E. coli and S. aureus depicted significant inhibition zones in comparison with 250 and 750 °C samples. Furthermore, molecular docking predictions of SnO₂ nanoparticles (NPs) were performed against active pocket of β-lactamase and DNA gyrase enzyme belonging to cell wall and nucleic acid biosynthetic pathway, respectively. The fabricated NPs showed good binding score against β-lactamase of both E. coli (- 5.71 kcal/mol) and S. aureus (- 11.83 kcal/mol) alongside DNA gyrase (- 9.57 kcal/mol; E. coli and - 8.61 kcal/mol; S. aureus). These in silico predictions suggested SnO₂ NPs as potential inhibitors for selected protein targets and will facilitate to have a clear understanding of their mechanism of action that may contribute toward new antibiotics discovery.

Diversity-Oriented Synthesis of N,N,O-Trisubstituted Hydroxylamines from Alcohols and Amines by N-O Bond Formation

Magnesium dialkylamides react with alcohol-derived 2-methyl-2-tetrahydropyranyl alkyl peroxides (MTHPs) in tetrahydrofuran at 0 °C to give N,N,O-trisubstituted hydroxylamines suitable for medicinal chemistry purposes in good to excellent yields. A wide range of secondary alkyl and aryl amines and primary and secondary alcohol-derived MTHPs are compatible with the described reaction which, coupled with the enormous diversity of commercially available alcohols and secondary amines, suggests broad applicability of the reaction in fragment-based library design.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Structural simplification: an efficient strategy in lead optimization

The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery and development. Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design by avoiding "molecular obesity". The structural simplification of large or complex lead compounds by truncating unnecessary groups can not only improve their synthetic accessibility but also improve their pharmacokinetic profiles, reduce side effects and so on. This review will summarize the application of structural simplification in lead optimization. Numerous case studies, particularly those involving successful examples leading to marketed drugs or drug-like candidates, will be introduced and analyzed to illustrate the design strategies and guidelines for structural simplification.

Target 2035: probing the human proteome

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

Ranking Hits From Saturation Transfer Difference Nuclear Magnetic Resonance-Based Fragment Screening

Fragment-based screening is an established route to identify low-molecular-weight molecules to generate high-affinity inhibitors in drug discovery. The affinities of these early hits from fragment screenings require a highly sensitive biophysical screening technique. Saturation transfer difference (STD) nuclear magnetic resonance (NMR) is one of the most popular methods owing to its high sensitivity for low-affinity ligands. It would be highly beneficial if rank-ordering of hits according to their affinity from an initial or counter-screen could be performed—a selection criterion found in the literature. We applied Complete Relaxation and Conformational Exchange Matrix (CORCEMA) theory adapted for saturation transfer (ST) measurements (CORCEMA-ST) calculations to predict STD NMR results from a large set of fragment/receptor pairs to investigate the boundaries under which the assumption holds true that a high STD effect can be applied to select for higher-affinity fragments. Overall, we come to the conclusion that this assumption is invalid.

Binding mode information improves fragment docking

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.

Ultra-large library docking for discovering new chemotypes

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

Computational analysis of the interactions of a novel cephalosporin derivative with β-lactamases

Background

One of the main concerns of the modern medicine is the frightening spread of antimicrobial resistance caused mainly by the misuse of antibiotics. The researchers worldwide are actively involved in the search for new classes of antibiotics, and for the modification of known molecules in order to face this threatening problem. We have applied a computational approach to predict the interactions between a new cephalosporin derivative containing an additional β-lactam ring with different substituents, and several serine β-lactamases representative of the different classes of this family of enzymes.

Results

The results of the simulations, performed by using a covalent docking approach, has shown that this compound, although able to bind the selected β-lactamases, has a different predicted binding score for the two β-lactam rings, suggesting that one of them could be more resistant to the attack of these enzymes and stay available to perform its bactericidal activity.

Conclusions

The detailed analysis of the complexes obtained by these simulations suggests possible hints to modulate the affinity of this compound towards these enzymes, in order to develop new derivatives with improved features to escape to degradation.

Electronic supplementary material

The online version of this article (10.1186/s12900-018-0092-5) contains supplementary material, which is available to authorized users.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.

A Greener and Efficient Method for Nucleophilic Aromatic Substitution of Nitrogen-Containing Fused Heterocycles

A simple and efficient methodology for the nucleophilic aromatic substitution of nitrogen-containing fused heterocycles with interesting biological activities has been developed in an environmentally sound manner using polyethylene glycol (PEG-400) as the solvent, leading to the expected compounds in excellent yields in only five minutes.

Strategies to diversify natural products for drug discovery

Natural product libraries contain specialized metabolites derived from plants, animals, and microorganisms that play a pivotal role in drug discovery due to their immense structural diversity and wide variety of biological activities. The strategies to greatly extend natural product scaffolds through available biological and chemical approaches offer unique opportunities to access a new series of natural product analogues, enabling the construction of diverse natural product-like libraries. The affordability of these structurally diverse molecules has been a crucial step in accelerating drug discovery. This review provides an overview of various approaches to exploit the diversity of compounds for natural product-based drug development, drawing upon a series of examples to illustrate each strategy.

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC₅₀ values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.

Ligand Discovery for a Peptide-Binding GPCR by Structure-Based Screening of Fragment- and Lead-Like Chemical Libraries

Peptide-recognizing G protein-coupled receptors (GPCRs) are promising therapeutic targets but often resist drug discovery efforts. Determination of crystal structures for peptide-binding GPCRs has provided opportunities to explore structure-based methods in lead development. Molecular docking screens of two chemical libraries, containing either fragment- or lead-like compounds, against a neurotensin receptor 1 crystal structure allowed for a comparison between different drug development strategies for peptide-binding GPCRs. A total of 2.3 million molecules were screened computationally, and 25 fragments and 27 leads that were top-ranked in each library were selected for experimental evaluation. Of these, eight fragments and five leads were confirmed as ligands by surface plasmon resonance. The hit rate for the fragment screen (32%) was thus higher than for the lead-like library (19%), but the affinities of the fragments were ∼100-fold lower. Both screens returned unique scaffolds and demonstrated that a crystal structure of a stabilized peptide-binding GPCR can guide the discovery of small-molecule agonists. The complementary advantages of exploring fragment- and lead-like chemical space suggest that these strategies should be applied synergistically in structure-based screens against challenging GPCR targets.

Counting on natural products for drug design

Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections for screening and achieving selective target modulation. Here we discuss natural-product-inspired drug discovery with a focus on recent advances in the design of synthetically tractable small molecules that mimic nature's chemistry. We highlight the potential of innovative computational tools in processing structurally complex natural products to predict their macromolecular targets and attempt to forecast the role that natural-product-derived fragments and fragment-like natural products will play in next-generation drug discovery.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Docking and Linking of Fragments To Discover Jumonji Histone Demethylase Inhibitors

Development of tool molecules that inhibit Jumonji demethylases allows for the investigation of cancer-associated transcription. While scaffolds such as 2,4-pyridinedicarboxylic acid (2,4-PDCA) are potent inhibitors, they exhibit limited selectivity. To discover new inhibitors for the KDM4 demethylases, enzymes overexpressed in several cancers, we docked a library of 600,000 fragments into the high-resolution structure of KDM4A. Among the most interesting chemotypes were the 5-aminosalicylates, which docked in two distinct but overlapping orientations. Docking poses informed the design of covalently linked fragment compounds, which were further derivatized. This combined approach improved affinity by ∼ 3 log-orders to yield compound 35 (Ki = 43 nM). Several hybrid inhibitors were selective for KDM4C over the related enzymes FIH, KDM2A, and KDM6B while lacking selectivity against the KDM3 and KDM5 subfamilies. Cocrystal structures corroborated the docking predictions. This study extends the use of structure-based docking from fragment discovery to fragment linking optimization, yielding novel KDM4 inhibitors.

Endless Resistance. Endless Antibiotics?

The practice of medicine was profoundly transformed by the introduction of the antibiotics (compounds isolated from Nature) and the antibacterials (compounds prepared by synthesis) for the control of bacterial infection. As a result of the extraordinary success of these compounds over decades of time, a timeless biological activity for these compounds has been presumed. This presumption is no longer. The inexorable acquisition of resistance mechanisms by bacteria is retransforming medical practice. Credible answers to this dilemma are far better recognized than they are being implemented. In this perspective we examine (and in key respects, reiterate) the chemical and biological strategies being used to address the challenge of bacterial resistance.

Fragment-Based Discovery of Subtype-Selective Adenosine Receptor Ligands from Homology Models

Fragment-based lead discovery (FBLD) holds great promise for drug discovery, but applications to G protein-coupled receptors (GPCRs) have been limited by a lack of sensitive screening techniques and scarce structural information. If virtual screening against homology models of GPCRs could be used to identify fragment ligands, FBLD could be extended to numerous important drug targets and contribute to efficient lead generation. Access to models of multiple receptors may further enable the discovery of fragments that bind specifically to the desired target. To investigate these questions, we used molecular docking to screen >500 000 fragments against homology models of the A3 and A1 adenosine receptors (ARs) with the goal to discover A3AR-selective ligands. Twenty-one fragments with predicted A3AR-specific binding were evaluated in live-cell fluorescence-based assays; of eight verified ligands, six displayed A3/A1 selectivity, and three of these had high affinities ranging from 0.1 to 1.3 μM. Subsequently, structure-guided fragment-to-lead optimization led to the identification of a >100-fold-selective antagonist with nanomolar affinity from commercial libraries. These results highlight that molecular docking screening can guide fragment-based discovery of selective ligands even if the structures of both the target and antitarget receptors are unknown. The same approach can be readily extended to a large number of pharmaceutically important targets.

Virtual Screening for UDP-Galactopyranose Mutase Ligands Identifies a New Class of Antimycobacterial Agents

Galactofuranose (Galf) is present in glycans critical for the virulence and viability of several pathogenic microbes, including Mycobacterium tuberculosis, yet the monosaccharide is absent from mammalian glycans. Uridine 5'-diphosphate-galactopyranose mutase (UGM) catalyzes the formation of UDP-Galf, which is required to produce Galf-containing glycoconjugates. Inhibitors of UGM have therefore been sought, both as antimicrobial leads and as tools to delineate the roles of Galf in cells. Obtaining cell permeable UGM probes by either design or high throughput screens has been difficult, as has elucidating how UGM binds small molecule, noncarbohydrate inhibitors. To address these issues, we employed structure-based virtual screening to uncover new inhibitor chemotypes, including a triazolothiadiazine series. These compounds are among the most potent antimycobacterial UGM inhibitors described. They also facilitated determination of a UGM-small molecule inhibitor structure, which can guide optimization. A comparison of results from the computational screen and a high-throughput fluorescence polarization (FP) screen indicated that the scaffold hits from the former had been evaluated in the FP screen but missed. By focusing on promising compounds, the virtual screen rescued false negatives, providing a blueprint for generating new UGM probes and therapeutic leads.

Charting a Path to Success in Virtual Screening

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.