Predicting Molecular Targets for Small-Molecule Drugs with a Ligand-Based Interaction Fingerprint Approach

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Profiling the interaction of 1-phenylbenzimidazoles to cyclooxygenases

In the design of 1-phenylbenzimidazoles as model cyclooxygenase (COX) inhibitors, docking to a series of crystallographic COX structures was performed to evaluate their potential for high-affinity binding and to reproduce the interaction profile of well-known COX inhibitors. The effect of ligand-specific induced fit on the calculations was also studied. To quantitatively compare the pattern of interactions of model compounds to the profile of several cocrystallized COX inhibitors, a geometric parameter, denominated ligand-receptor contact distance (LRCD), was developed. The interaction profile of several model complexes showed similarity to the profile of COX complexes with inhibitors such as iodosuprofen, iodoindomethacin, diclofenac, and flurbiprofen. Shaping of high-affinity binding sites upon ligand-specific induced fit mostly determined both the affinity and the binding mode of the ligands in the docking calculations. The results suggest potential of 1-phenylbenzimidazole derivatives as COX inhibitors on the basis of their predicted affinity and interaction profile to COX enzymes. The analyses also provided insights into the role of induced fit in COX enzymes. While inhibitors produce different local structural changes at the COX ligand binding site, induced fit allows inhibitors in diverse chemical classes to share characteristic interaction patterns that ensure key contacts to be achieved. Different interaction patterns may also be associated with different inhibitory mechanisms.

Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer's disease

Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease.

Web-Based Tools for Polypharmacology Prediction

Drug promiscuity or polypharmacology is the ability of small molecules to interact with multiple protein targets simultaneously. In drug discovery, understanding the polypharmacology of potential drug molecules is crucial to improve their efficacy and safety, and to discover the new therapeutic potentials of existing drugs. Over the past decade, several computational methods have been developed to study the polypharmacology of small molecules, many of which are available as Web services. In this chapter, we review some of these Web tools focusing on ligand based approaches. We highlight in particular our recently developed polypharmacology browser (PPB) and its application for finding the side targets of a new inhibitor of the TRPV6 calcium channel.

Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints

Background

Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied.

Results

The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests.

Conclusion

A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0302-y) contains supplementary material, which is available to authorized users.

Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists

Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.

The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data

Background

Several web-based tools have been reported recently which predict the possible targets of a small molecule by similarity to compounds of known bioactivity using molecular fingerprints (fps), however predictions in each case rely on similarities computed from only one or two fps. Considering that structural similarity and therefore the predicted targets strongly depend on the method used for comparison, it would be highly desirable to predict targets using a broader set of fps simultaneously.

Results

Herein, we present the polypharmacology browser (PPB), a web-based platform which predicts possible targets for small molecules by searching for nearest neighbors using ten different fps describing composition, substructures, molecular shape and pharmacophores. PPB searches through 4613 groups of at least 10 same target annotated bioactive molecules from ChEMBL and returns a list of predicted targets ranked by consensus voting scheme and p value. A validation study across 670 drugs with up to 20 targets showed that combining the predictions from all 10 fps gives the best results, with on average 50% of the known targets of a drug being correctly predicted with a hit rate of 25%. Furthermore, when profiling a new inhibitor of the calcium channel TRPV6 against 24 targets taken from a safety screen panel, we observed inhibition in 5 out of 5 targets predicted by PPB and in 7 out of 18 targets not predicted by PPB. The rate of correct (5/12) and incorrect (0/12) predictions for this compound by PPB was comparable to that of other web-based prediction tools.

Conclusion

PPB offers a versatile platform for target prediction based on multi-fingerprint comparisons, and is freely accessible at www.gdb.unibe.ch as a valuable support for drug discovery.Graphical abstract

Computational approaches in target identification and drug discovery

In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

Free energy calculation provides insight into the action mechanism of selective PARP-1 inhibitor

Selective poly (ADP-ribose) polymerase (PARP)-1 inhibitor represents promising therapy against cancers with a good balance between efficacy and safety. Owing to the conserved structure between PARP-1 and PARP-2, most of the clinical and experimental drugs show equivalent inhibition against both targets. Most recently, it's disclosed a highly selective PARP-1 inhibitor (NMS-P118) with promising pharmacokinetic properties. Herein, we combined molecular simulation with free energy calculation to gain insights into the selective mechanism of NMS-P118. Our results suggest the reduction of binding affinity for PARP-2 is attributed to the unfavorable conformational change of protein, which is accompanied by a significant energy penalty. Alanine-scanning mutagenesis study further reveals the important role for a tyrosine residue of donor loop (Tyr889(PARP-1) and Tyr455(PARP-2)) in contributing to the ligand selectivity. Retrospective structural analysis indicates the ligand-induced movement of Tyr455(PARP-2) disrupts the intra-molecule hydrogen bonding network, which partially accounts for the "high-energy" protein conformation in the presence of NMS-P118. Interestingly, such effect isn't observed in other non-selective PARP inhibitors including BMN673 and A861695, which validates the computational prediction. Our work provides energetic insight into the subtle variations in the crystal structures and could facilitate rational design of new selective PARP inhibitor.