High-throughput and in silico screenings in drug discovery

Specifically targeting antimicrobial peptides for inhibition of Candidatus Liberibacter asiaticus

AIMS

Huanglongbing (citrus greening) is a plant disease putatively caused by the unculturable Gram-negative bacterium Candidatus Liberibacter asiaticus (CLas), and it has caused severe damage to citrus plantations worldwide. There are no definitive treatments for this disease, and conventional disease control techniques have shown limited efficacy. This work presents an in silico evaluation of using specifically targeting anti-microbial peptides (STAMPs) consisting of a targeting segment and an antimicrobial segment to inhibit citrus greening by inhibiting the BamA protein of CLas, which is an outer membrane protein crucial for bacterial viability.

METHODS AND RESULTS

Initially, a set of peptides with a high affinity toward BamA protein were screened and evaluated via molecular docking and molecular dynamics simulations and were verified in vitro via bio-layer interferometry (BLI). In silico studies and BLI experiments indicated that two peptides, HASP2 and HASP3, showed stable binding to BamA. Protein structures for STAMPs were created by fusing known anti-microbial peptides (AMPs) with the selected short peptides. The binding of STAMPs to BamA was assessed using molecular docking and binding energy calculations. The attachment of high-affinity short peptides significantly reduced the free energy of binding for AMPs, suggesting that it would make it easier for the STAMPs to bind to BamA. Efficacy testing in vitro using a closely related CLas surrogate bacterium showed that STAMPs had greater inhibitory activity than AMP alone.

CONCLUSIONS

In silico and in vitro results indicate that the STAMPs can inhibit CLas surrogate Rhizobium grahamii more effectively compared to AMPs, suggesting that STAMPs can achieve better inhibition of CLas, potentially via enhancing the site specificity of AMPs.

GLDM: hit molecule generation with constrained graph latent diffusion model

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.

Synthesis, in silico screening, and biological evaluation of novel pyridine congeners as anti-epileptic agents targeting AMPA (α-amino-3-hydroxy-5-methylisoxazole) receptors

The research involves the synthesis of a series of new pyridine analogs 5(i-x) and their evaluation for anti-epileptic potential using in silico and in vivo models. Synthesis of the compounds was accomplished by using the Vilsmeier-Haack reaction principle. AutoDock 4.2 was used for their in silico screening against AMPA (-amino-3-hydroxy-5-methylisoxazole) receptor (PDB ID:3m3f). For in vivo testing, the maximal electroshock seizure (MES) model was used. The physicochemical, pharmacokinetic, drug-like, and drug-score features of all synthesized compounds were assessed using the online Swiss ADME and Protein Plus software. The in silico results showed that all the synthesized compounds 5(i-x) had 1-3 interactions and affinities ranging from -6.5 to -8.0 kJ/mol with the targeted receptor compared to the binding affinities of the standard drug phenytoin and the original ligand of the target (P99), which were -7.6 and -6.8 kJ/mol, respectively. In vivo study results showed that the compound 5-Carbamoyl-2-formyl-1-[2-(4-nitrophenyl)-2-oxo-ethyl]-pyridinium gave 60% protection against epileptic seizures compared to 59% protection afforded by regular phenytoin. All of them met Lipinski's rule of five and had drug-likeness and drug score values of 0.55 and 0.8, respectively, making them chemically and functionally like phenytoin. According to the findings of the studies, the synthesized derivatives have the potential to be employed as a stepping stone in the development of novel anti-epileptic drugs.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Multi-structural molecular docking (MOD) combined with molecular dynamics reveal the structural requirements of designing broad-spectrum inhibitors of SARS-CoV-2 entry to host cells

New variants of SARS-CoV-2 that can escape immune response continue to emerge. Consequently, there is an urgent demand to design small molecule therapeutics inhibiting viral entry to host cells to reduce infectivity rate. Despite numerous in silico and in situ studies, the structural requirement of designing viral-entry inhibitors effective against multiple variants of SARS-CoV-2 has yet to be described. Here we systematically screened the binding of various natural products (NPs) to six different SARS-CoV-2 receptor-binding domain (RBD) structures. We demonstrate that Multi-structural Molecular Docking (MOD) combined with molecular dynamics calculations allowed us to predict a vulnerable site of RBD and the structural requirement of ligands binding to this vulnerable site. We expect that our findings lay the foundation for in silico screening and identification of lead molecules to guide drug discovery into designing new broad-spectrum lead molecules to counter the threat of future variants of SARS-CoV-2.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network

MOTIVATION

The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction.

RESULTS

In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets.

AVAILABILITY AND IMPLEMENTATION

Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Characterization and chemical modulation of p62/SQSTM1/Sequestosome-1 as an autophagic N-recognin

In the Arg/N-degron pathway, single N-terminal (Nt) residues function as N-degrons recognized by UBR box-containing N-recognins that induce substrate ubiquitination and proteasomal degradation. Recent studies led to the discovery of the autophagic Arg/N-degron pathway, in which the autophagic receptor p62/SQSTM1/Sequestosome-1 acts as an N-recognin that binds the Nt-Arg and other destabilizing residues as N-degrons. Upon binding to Nt-Arg, p62 undergoes self-polymerization associated with its cargoes, accelerating the macroautophagic delivery of p62-cargo complexes to autophagosomes leading to degradation by lysosomal hydrolases. This autophagic mechanism is emerging as an important pathway that modulates the lysosomal degradation of various biomaterial ranging from protein aggregates and subcellular organelles to invading pathogens. Chemical mimics of the physiological N-degrons were developed to exert therapeutic efficacy in pathophysiological processes associated with neurodegeneration and other related diseases. Here, we describe the methods to monitor the activities of p62 in a dual role as an N-recognin and an autophagic receptor. The topic includes self-polymerization (for cargo condensation), its interaction with LC3 on autophagic membranes (for cargo targeting), and the degradation of p62-cargo complexes by lysosomal hydrolases. We also discuss the development and use of small molecule mimics of N-degrons that modulate p62-dependent macroautophagy in biological and pathophysiological processes.

In-silico identification of novel inhibitors for human Aurora kinase B form the ZINC database using molecular docking-based virtual screening

Introduction: Aurora kinase enzymes play critical functions in mammals. Aurora kinases are mitotic regulators that are involved in a variety of processes during cell division. The overexpression of these enzymes is associated with tumour formation and is symptomatic of clinical circumstances in cancer patients who have been diagnosed.

Materials and methods: The current study reports an in-silico virtual screening (VS) and molecular docking analysis of 2500 compounds retrieved from the ZINC database and five current clinical trial compounds against Aurora Kinase B using AutoDock Vina to identify potential inhibitors.

Results and discussion: The top six compounds that resulted from the screening were ZINC00190959, ZINC07889110, ZINC0088285, ZINC01404326, ZINC00882846 and ZINC08813187, which showed lower free energy of binding (FEB) against the target protein binding pocket. The FEB were as follows: -11.92, -11.85, -11.46, -11.33, -11.21 and -11.1 kcal/mol, using AutoDock, and -11.7, -11.5, -11.2, -11.0, -10.8 and -10.6 kcal/mol for AutoDock Vina, respectively. These findings were superior to those obtained with the co-crystallized ligand VX-680, with a -7.5 kcal/mol and the current clinical trial drug. Finally, using a VS and molecular docking approach, novel Aurora kinase B inhibitors were effectively identified from the ZINC database fulfilling the Lipinski rule of five with low FEB and functional molecular interactions with the target protein.

Conclusion: The findings suggest that the six compounds could be used as a potential agent for cancer treatments.

Graphical abstract

Machine intelligence-driven framework for optimized hit selection in virtual screening

Virtual screening (VS) aids in prioritizing unknown bio-interactions between compounds and protein targets for empirical drug discovery. In standard VS exercise, roughly 10% of top-ranked molecules exhibit activity when examined in biochemical assays, which accounts for many false positive hits, making it an arduous task. Attempts for conquering false-hit rates were developed through either ligand-based or structure-based VS separately; however, nonetheless performed remarkably well. Here, we present an advanced VS framework—automated hit identification and optimization tool (A-HIOT)—comprises chemical space-driven stacked ensemble for identification and protein space-driven deep learning architectures for optimization of an array of specific hits for fixed protein receptors. A-HIOT implements numerous open-source algorithms intending to integrate chemical and protein space leading to a high-quality prediction. The optimized hits are the selective molecules which we retrieve after extreme refinement implying chemical space and protein space modules of A-HIOT. Using CXC chemokine receptor 4, we demonstrated the superior performance of A-HIOT for hit molecule identification and optimization with tenfold cross-validation accuracies of 94.8% and 81.9%, respectively. In comparison with other machine learning algorithms, A-HIOT achieved higher accuracies of 96.2% for hit identification and 89.9% for hit optimization on independent benchmark datasets for CXCR4 and 86.8% for hit identification and 90.2% for hit optimization on independent test dataset for androgen receptor (AR), thus, shows its generalizability and robustness. In conclusion, advantageous features impeded in A-HIOT is making a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS in finding the optimized hits for the desired receptor. The complete resource (framework) code is available at https://gitlab.com/neeraj-24/A-HIOT.

Graphical Abstract

Studying the characteristics of nanobody CDR regions based on sequence analysis in combination with 3D structures

Background

Single-domain antibodies or nanobodies have recently attracted much attention in research and applications because of their great potential and advantage over conventional antibodies. However, isolation of candidate nanobodies in the lab has been costly and time-consuming. Screening of leading nanobody candidates through synthetic libraries is a promising alternative, but it requires prior knowledge to control the diversity of the complementarity-determining regions (CDRs) while still maintaining functionality. In this work, we identified sequence characteristics that could contribute to nanobody functionality by analyzing three datasets, CDR1, CDR2, and CDR3.

Results

By classification of amino acids based on physicochemical properties, we found that two different amino acid groups were sufficient for CDRs. The nonpolar group accounted for half of the total amino acid composition in these sequences. Observation of the highest occurrence of each amino acid revealed that the usage of some important amino acids such as tyrosine and serine was highly correlated with the length of the CDR3. Amino acid repeat motifs were also under-represented and highly restricted as 3-mers. Inspecting the crystallographic data also demonstrated conservation in structural coordinates of dominant amino acids such as methionine, isoleucine, valine, threonine, and tyrosine and certain positions in the CDR1, CDR2, and CDR3 sequences.

Conclusions

We identified sequence characteristics that contributed to functional nanobodies including amino acid groups, the occurrence of each kind of amino acids, and repeat patterns. These results provide a simple set of rules to make it easier to generate desired candidates by computational means; also, they can be used as a reference to evaluate synthetic nanobodies.

Uncovering Streptomyces-Derived Compounds as Cosmeceuticals for the Development of Improved Skin Photoprotection Products: An In Silico Approach to Explore Multi-Targeted Agents

The search for novel photoprotective substances has become a challenge in cosmeceutical research. Streptomyces-derived compounds can serve as a promising source of photoprotective agents to formulate skin photoprotection products, such as sunscreens. This study aimed to identify specialized metabolites with the potential to modulate UV-induced cellular damage in the skin by identifying potential multi-target-directed ligands. Using a combination of ligand- and target-based virtual screening approaches, a public compound library comprising 6524 Streptomyces-derived specialized metabolites was studied for their photoprotective capability. The compounds were initially filtered by safety features and then examined for their ability to interact with key targets in the photodamage pathway by molecular docking. A set of 50 commercially available UV filters was used as the benchmark. The protein–ligand stability of selected Streptomyces-derived compounds was also studied by molecular dynamics (MD) simulations. From the compound library, 1981 compounds were found to meet the safety criteria for topically applied products, such as low skin permeability and low or non-toxicity-alerting substructures. A total of 34 compounds had promising binding scores against crucial targets involved in UV-induced photodamage, such as serotonin-receptor subtype 5-HT2A, platelet-activating factor receptor, IL-1 receptor type 1, epidermal growth factor receptor, and cyclooxygenase-2. Among these compounds, aspergilazine A and phaeochromycin F showed the highest ranked interactions with four of the five targets and triggered complex stabilization over time. Additionally, the predicted UV-absorbing profiles also suggest a UV-filtering effect. Streptomyces is an encouraging biological source of compounds for developing topical products. After in silico protein–ligand interactions, binding mode and stabilization of aspergilazine A and phaeochromycin F led to the discovery of potential candidates as photodamage multi-target inhibitors. Therefore, they can be further explored for the formulation of skin photoprotection products.

Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery

Drug Discovery is an active research area that demands great investments and generates low returns due to its inherent complexity and great costs. To identify potential therapeutic candidates more effectively, we propose protein–ligand with adversarial augmentations network (PLA-Net), a deep learning-based approach to predict target–ligand interactions. PLA-Net consists of a two-module deep graph convolutional network that considers ligands’ and targets’ most relevant chemical information, successfully combining them to find their binding capability. Moreover, we generate adversarial data augmentations that preserve relevant biological backgrounds and improve the interpretability of our model, highlighting the relevant substructures of the ligands reported to interact with the protein targets. Our experiments demonstrate that the joint ligand–target information and the adversarial augmentations significantly increase the interaction prediction performance. PLA-Net achieves 86.52% in mean average precision for 102 target proteins with perfect performance for 30 of them, in a curated version of actives as decoys dataset. Lastly, we accurately predict pharmacologically-relevant molecules when screening the ligands of ChEMBL and drug repurposing Hub datasets with the perfect-scoring targets.

Identification of the key functional genes in salt-stress tolerance of Cyanobacterium Phormidium tenue using in silico analysis

The development of artificial biocrust using cyanobacterium Phormidium tenue has been suggested as an effective strategy to prevent soil degradation. Here, a combination of in silico approaches with growth rate, photosynthetic pigment, morphology, and transcript analysis was used to identify specific genes and their protein products in response to 500 mM NaCl in P. tenue. The results show that 500 mM NaCl induces the expression of genes encoding glycerol-3-phosphate dehydrogenase (glpD) as a Flavoprotein, ribosomal protein S12 methylthiotransferase (rimO), and a hypothetical protein (sll0939). The constructed co-expression network revealed a group of abiotic stress-responsive genes. Using the Basic Local Alignment Search Tool (BLAST), the homologous proteins of rimO, glpD, and sll0939 were identified in the P. tenue genome. Encoded proteins of glpD, rimO, and DUF1622 genes, respectively, contain (DAO and DAO C), (UPF0004, Radical SAM and TRAM 2), and (DUF1622) domains. The predicted ligand included 22B and MG for DUF1622, FS5 for rimO, and FAD for glpD protein. There was no direct disruption in ligand-binding sites of these proteins by Na+, Cl-, or NaCl. The growth rate, photosynthetic pigment, and morphology of P. tenue were investigated, and the result showed an acceptable tolerance rate of this microorganism under salt stress. The quantitative real-time polymerase chain reaction (qRT-PCR) results revealed the up-regulation of glpD, rimO, and DUF1622 genes under salt stress. This is the first report on computational and experimental analyses of the glpD, rimO, and DUF1622 genes in P. tenue under salt stress to the best of our knowledge.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03050-w.

A glutathione disulfide-sensitive Janus nanomachine controlled by an enzymatic AND logic gate for smart delivery

This work describes the assembly of a novel enzyme-controlled nanomachine operated through an AND Boolean logic gate for on-command delivery. The nanodevice was constructed on Au-mesoporous silica Janus nanoparticles capped with a thiol-sensitive gate-like molecular ensemble on the mesoporous face and functionalized with glutathione reductase on the gold face. This autonomous nanomachine employed NADPH and glutathione disulfide as input chemical signals, leading to the enzymatic production of reduced glutathione that causes the disruption of the gating mechanism on the mesoporous face and the consequent payload release as an output signal. The nanodevice was successfully used for the autonomous release of doxorubicin in HeLa cancer cells and RAW 264.7 macrophage cells.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.

A High-throughput Approach to Identify Effective Systemic Agents for the Treatment of Anaplastic Thyroid Carcinoma

BACKGROUND

Despite the use of aggressive multimodality treatment, most anaplastic thyroid carcinoma (ATC) patients die within a year of diagnosis. Although the combination of BRAF and MEK inhibitors has recently been approved for use in BRAF-mutated ATC, they remain effective in a minority of patients who are likely to develop drug resistance. There remains a critical clinical need for effective systemic agents for ATC with a reasonable toxicity profile to allow for rapid translational development.

MATERIAL AND METHODS

Twelve human thyroid cancer cell lines with comprehensive genomic characterization were used in a high-throughput screening (HTS) of 257 compounds to select agents with maximal growth inhibition. Cell proliferation, colony formation, orthotopic thyroid models, and patient-derived xenograft (PDX) models were used to validate the selected agents.

RESULTS

Seventeen compounds were effective, and docetaxel, LBH-589, and pralatrexate were selected for additional in vitro and in vivo analysis as they have been previously approved by the US Food and Drug Administration for other cancers. Significant tumor growth inhibition (TGI) was detected in all tested models treated with LBH-589; pralatrexate demonstrated significant TGI in the orthotopic papillary thyroid carcinoma model and 2 PDX models; and docetaxel demonstrated significant TGI only in the context of mutant TP53.

CONCLUSIONS

HTS identified classes of systemic agents that demonstrate preferential effectiveness against aggressive thyroid cancers, particularly those with mutant TP53. Preclinical validation in both orthotopic and PDX models, which are accurate in vivo models mimicking tumor microenvironment, may support initiation of early-phase clinical trials in non-BRAF mutated or refractory to BRAF/MEK inhibition ATC.

Phytochemicals: A Promising Weapon in the Arsenal against Antibiotic-Resistant Bacteria

The extensive usage of antibiotics and the rapid emergence of antimicrobial-resistant microbes (AMR) are becoming important global public health issues. Many solutions to these problems have been proposed, including developing alternative compounds with antimicrobial activities, managing existing antimicrobials, and rapidly detecting AMR pathogens. Among all of them, employing alternative compounds such as phytochemicals alone or in combination with other antibacterial agents appears to be both an effective and safe strategy for battling against these pathogens. The present review summarizes the scientific evidence on the biochemical, pharmacological, and clinical aspects of phytochemicals used to treat microbial pathogenesis. A wide range of commercial products are currently available on the market. Their well-documented clinical efficacy suggests that phytomedicines are valuable sources of new types of antimicrobial agents for future use. Innovative approaches and methodologies for identifying plant-derived products effective against AMR are also proposed in this review.

A semi-supervised learning framework for quantitative structure-activity regression modelling

MOTIVATION

Quantitative structure-activity relationship (QSAR) methods are increasingly used in assisting the process of preclinical, small molecule drug discovery. Regression models are trained on data consisting of a finite-dimensional representation of molecular structures and their corresponding target-specific activities. These supervised learning models can then be used to predict the activity of previously unmeasured novel compounds.

RESULTS

This work provides methods that solve three problems in QSAR modelling: (i) a method for comparing the information content between finite-dimensional representations of molecular structures (fingerprints) with respect to the target of interest, (ii) a method that quantifies how the accuracy of the model prediction degrades as a function of the distance between the testing and training data and (iii) a method to adjust for screening dependent selection bias inherent in many training datasets. For example, in the most extreme cases, only compounds which pass an activity-dependent screening threshold are reported. A semi-supervised learning framework combines (ii) and (iii) and can make predictions, which take into account the similarity of the testing compounds to those in the training data and adjust for the reporting selection bias. We illustrate the three methods using publicly available structure-activity data for a large set of compounds reported by GlaxoSmithKline (the Tres Cantos AntiMalarial Set, TCAMS) to inhibit asexual in vitro Plasmodium falciparum growth.

AVAILABILITYAND IMPLEMENTATION

https://github.com/owatson/PenalizedPrediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks

The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 97.7% in the Receiver Operating Characteristic curve (ROC-AUC) and 65.5% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained three (3) potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.

Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds

Molecular docking is a useful and powerful computational method for the identification of potential interactions between small molecules and pharmacological targets. In reverse docking, the ability of one or a few compounds to bind a large dataset of proteins is evaluated in silico. This strategy is useful for identifying molecular targets of orphan bioactive compounds, proposing new molecular mechanisms, finding alternative indications of drugs, or predicting drug toxicity. Herein, we describe a detailed reverse docking protocol for the identification of potential targets for 4-hydroxycoumarin (4-HC). Our results showed that RAC1 is a target of 4-HC, which partially explains the biological activities of 4-HC on cancer cells. The strategy reported here can be easily applied to other compounds and protein datasets.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Molecular Docking Using Quantum Mechanical-Based Methods

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

Alignment-independent 3D-QSAR and molecular docking studies of tacrine-4-oxo-4H-Chromene hybrids as anti-Alzheimer's agents

A series of novel tacrine derivatives as multifunctional agents with potential inhibitory effects on both acetylcholinesterase(AChE) and butyrylcholinesterase (BuChE) enzymes for the treatment of Alzheimer's disease(AD), were applied to alignment independent 3D-QSAR methods using Pentacle software. In this studies, GRID-independent molecular descriptors (GRIND) analysis have been applied to characterize important interactions between enzymes and the studied compounds. Two H-bond acceptor groups as well as hydrophobic properties of tacrine rings for AChE and two H-bond acceptor on the carbonyl group of chromene and NH of amid group for BuChE, with positive effects on their inhibitory potency have been identified. The obtained 3D-QSAR models have been analyzed and validated. The statistical quality of the QSAR model for AChE, r² = 0.87, q² = 0.56 and for BuChE, r² = 0.96, q² = 0.70 was resulted. Using these models, novel structures have been designed and pIC₅₀ of them were predicted. Molecular docking studies were also conducted on AChE (1ACJ) and BuChE (4BDS) and promising results in good agreement with 3D-QSAR studies were obtained.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors

Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn-ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.

In silico insights into prediction and analysis of potential novel pyrrolopyridine analogs against human MAPKAPK-2: a new SAR-based hierarchical clustering approach

In the present study, we have focused on to elucidate potential bioactive pyrrolopyridine (PYP23) analogs against human mitogen-activated protein kinase-activated protein kinase-2 (MK-2). Here, in silico methods and computational systems biology tools were used as rational strategies to predict novel PYP23 analogs against the MK-2. Initially, crystal structure (PDB-ID: 2P3G) consists steriochemical conflicts were rectified by structure-optimization approaches using the Modeller program, and a new optimized-high resolution model was generated. The stereochemical qualities of the predicted MK-2 model were judged; these showed that the model was reliable for docking assessments. SAR-based bioactivity analysis showed that among the 197 datasets only 15 candidates contained bioactivity data and were accepted as probable MK-2 inhibitors. Virtual screening and docking strategies of dataset compounds against the ligand-binding domain of MK-2 recognized 13 composites containing high binding affinity than known compounds. Furthermore, the comparative structure clustering, in silico toxicogenomics and QSAR-based anticancer properties prediction approaches were successful in the recognition of five best potential compounds such as 60118340, 60118338, 60117736, 60118473 and 60118322, which have great anticancer and drug-likeness with non-toxicity class indices. Leu⁷⁰, Glu¹³⁹, Leu¹⁴¹, Glu¹⁴⁵, Glu¹⁹⁰, Thr²⁰⁶ and Asp²⁰⁷ were found to be novel hotspot residues prominently involved in H-bonds framing with ligands. Interestingly, they have shown better molecular similarity with known bioactive PYP inhibitors. Thus, predicted five compounds can useful as possible chemotherapeutic agents for MK-2 and show similar molecular actions like known PYP inhibitors. Overall, these streamlined new methods may have great potential to reveal possible ligands toward other molecular targets and biomarkers.

Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.

Hydrogen Bonding: Between Strengthening the Crystal Packing and Improving Solubility of Three Haloperidol Derivatives

The purpose of this study is to confirm the impact of polar functional groups on inter and intra-molecular hydrogen bonding in haloperidol (HP) and droperidol (DP) and, hence, their effects on dissolution using a new approach. To confirm our theory, a new molecule: deshydroxy-haloperidol (DHP) was designed and its synthesis was requested from a contract laboratory. The molecule was then studied and compared to DP and HP. Unlike DHP, both the HP and DP molecules have hydrogen donor groups, therefore, DHP was used to confirm the relative effects of the hydrogen donor group on solubility and crystal packing. The solid dispersions of the three structurally related molecules: HP, DP, and DHP were prepared using PVPK30, and characterized using XRPD and IR. A comparative dissolution study was carried out in aqueous medium. The absence of a hydrogen bonding donor group in DHP resulted in an unexpected increase in its aqueous solubility and dissolution rate from solid dispersion, which is attributed to weaker crystal pack. The increased dissolution rate of HP and DP from solid dispersions is attributed to drug-polymer hydrogen bonding that interferes with the drug-drug intermolecular hydrogen bonding and provides thermodynamic stability of the dispersed drug molecules. The drug-drug intermolecular hydrogen bond is the driving force for precipitation and crystal packing.

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.

Pharmacological Targeting of AMP-Activated Protein Kinase and Opportunities for Computer-Aided Drug Design

As a central regulator of metabolism, the AMP-activated protein kinase (AMPK) is an established therapeutic target for metabolic diseases. Beyond the metabolic area, the number of medical fields that involve AMPK grows continuously, expanding the potential applications for AMPK modulators. Even though indirect AMPK activators are used in the clinics for their beneficial metabolic outcome, the few described direct agonists all failed to reach the market to date, which leaves options open for novel targeting methods. As AMPK is not actually a single molecule and has different roles depending on its isoform composition, the opportunity for isoform-specific targeting has notably come forward, but the currently available modulators fall short of expectations. In this review, we argue that with the amount of available structural and ligand data, computer-based drug design offers a number of opportunities to undertake novel and isoform-specific targeting of AMPK.

Molecular docking and multivariate analysis of xanthones as antimicrobial and antiviral agents

Xanthones are secondary metabolites which have drawn considerable interest over the last decades due to their antimicrobial properties, among others. A great number of this kind of compounds has been therefore reported, but there is a limited amount of studies on screening for biological activity. Thus, as part of our research on antimicrobial agents of natural origin, a set of 272 xanthones were submitted to molecular docking (MD) calculations with a group of seven fungal and two viral enzymes. The results indicated that prenylated xanthones are important hits for inhibition of the analyzed enzymes. The MD scores were also analyzed by multivariate statistics. Important structural details were found to be crucial for the inhibition of the tested enzymes by the xanthones. In addition, the classification of active xanthones can be achieved by statistical analysis on molecular docking scores by an affinity-antifungal activity relationship approach. The obtained results therefore are a suitable starting point for the development of antifungal and antiviral agents based on xanthones.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Design of chemical libraries for screening

IMPORTANCE OF THE FIELD

The ultimate goal of discovery screening is to have a fast and cost-effective strategy to meet the demands of producing high-content lead series with improved prospects for clinical success. While high-throughput screening (HTS) dominates the drug discovery landscape, other processes and technologies have emerged, including high-content screening and fragment-based design to provide alternatives that may be more suitable for certain targets. There has been a growing interest in reducing the number of compounds to be screened to prevent the escalation in the costs, time and resources associated with HTS campaigns. Library design plays a central role in these efforts.

AREAS COVERED IN THIS REVIEW

This opinion provides a survey of some recent developments in the diversity based library design process, but within a historical context. In particular, the importance of chemotyping and substructure analysis and the challenges presented by novel lead discovery technologies that require the design of libraries for screening are discussed.

WHAT THE READER WILL GAIN

Readers will gain an appreciation of some developments in the field of library design and the factors that are driving the development of new library design technologies; specifically, challenges presented for chemoinformatics with the novel screening technologies in diversity based screening and compound filtering.

TAKE HOME MESSAGE

Chemotyping and substrutural analysis are techniques that have been underutilized in the process of library design. However, they offer a direct way to evaluate libraries and have been successfully used to develop predictive methodologies. Tools are available to this end, but the full power of the approach has not been realized yet.