Virtual screening strategies in drug discovery

The identification of novel therapeutic targets and characterization of their 3D structures is increasing at a dramatic rate. Computational screening methods continue to be developed and improved as credible and complementary alternatives to high-throughput biochemical compound screening (HTS). While the majority of drug candidates currently being developed have been found using HTS methods, high-throughput docking and pharmacophore-based searching algorithms are gaining acceptance and becoming a major source of lead molecules in drug discovery. Refinements and optimization of high-throughput docking methods have lead to improvements in reproducing experimental data and in hit rates obtained, validating their use in hit identification. In parallel with virtual screening methods, concomitant developments in cheminformatics including identification, design and manipulation of drug-like small molecule libraries have been achieved. Herein, currently used in silico screening techniques and their utility on a comparative and target dependent basis is discussed.     

Computational Chemistry in Lead Identification,Library Design and Lead Optimisation

Abstract

We describe a variety of the computational techniques which we use in the drug discovery and design process. Some of these computational methods are designed to support the new experimental technologies of high-throughput screening and combinatorial chemistry. We also consider some new approaches to problems of long-standing interest such as protein-ligand docking and the prediction of free energies of binding.     

Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.     

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor

Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.     

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.     

High-throughput docking for lead generation

Recent improvements in flexible docking technology may lead to a bigger role for computational methods in lead discovery. Although fast and accurate computational prediction of binding affinities for an arbitrary molecule is still beyond the limits of current methods, the docking and screening procedures can select small sets of likely lead candidates from large libraries of either commercially or synthetically available compounds.     

Advances and applications of binding affinity prediction methods in drug discovery

Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.     

Statistical decoding of potent pools based on chemical structure

Pooling experiments are used as a cost-effective approach for screening chemical compounds as part of the drug discovery process in pharmaceutical companies. When a biologically potent pool is found, the goal is to decode the pool, i.e., to determine which of the individual compounds are potent. We propose augmenting the data on pooled testing with information on the chemical structure of compounds in order to complete the decoding process. This proposal is based on the well-known relationship between biological potency of a compound and its chemical structure. Application to real data from a drug discovery process at GlaxoSmithKline reveals a 100% increase in hit rate, namely, the number of potent compounds identified divided by the number of tests required.     

Large-scale integrated super-computing platform for next generation virtual drug discovery

Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand-receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed.     

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.     

Illuminating drug discovery with biological pathways

Systems biology promises to impact significantly on the drug discovery process. One of its ultimate goals is to provide an understanding of the complete set of molecular mechanisms describing an organism. Although this goal is a long way off, many useful insights can already come from currently available information and technology. One of the biggest challenges in drug discovery today is the high attrition rate: many promising candidates prove ineffective or toxic owing to a poor understanding of the molecular mechanisms of biological systems they target. A "systems" approach can help identify pathways related to a disease and can suggest secondary effects of drugs that might cause these problems and thus ultimately improve the drug discovery pipeline.     

Identification of novel serotonin 2C receptor ligands by sequential virtual screening

Pharmacophore modelling, docking and virtual screening have become important tool in drug discovery process. Serotonin 2C (5-HT_2C) receptor ligands have got major attention for their therapeutic uses as antidepressant and anorectic agents. Two step pharmacophore and docking based virtual screening was done using 5-HT_2C agonists. Two common feature pharmacophore directed virtual hits had submicromolar activity. Refined pharmacophore with excluded volumes was constructed and combined with homology model based docking. Best hit from this virtual screening showed IC₅₀ of 20.1 nM. Similarity search of this hit compound resulted more active ligand with 7.8 nM activity.     

New targets for an old drug

Methotrexate has been a clinical agent used in cancer, immunosuppression, rheumatoid arthritis and other highly proliferative diseases for many years, yet its underlying molecular mechanism of action in these therapeutic areas is still unclear. We present a chemical proteomics approach that uses ultra-sensitive mass spectrometry coupled to an inverse protein-ligand docking computational technique to unravel the mechanism of action of this drug. Using methotrexate tethered to a solid support we were able to isolate a signficant number of proteins. We effectively captured a large portion of the de novo purine metaolome and propose a pathway architecture similar to that seen in signaling pathways and consistent with substrate channeling. More importantly, we were able to capture protein targets that could potentially provide new insights into the mechanism of action of methotrexate in rheumatoid arthritis and immunosuppression. The application of this approach to other drugs and drug candidates may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity. These results demonstrate that this proteomics technology could play an important role in drug discovery and development since it allows monitoring of the interactions between a drug and the protein content of a cell.     

Optimization of a pyrazole hit from FBDD into a novel series of indazoles as ketohexokinase inhibitors

A series of indazoles have been discovered as KHK inhibitors from a pyrazole hit identified through fragment-based drug discovery (FBDD). The optimization process guided by both X-ray crystallography and solution activity resulted in lead-like compounds with good pharmaceutical properties.     

Discovery of potential novel microsomal triglyceride transfer protein inhibitors via virtual screening of pharmacophore modelling and molecular docking

In order to identify potential natural inhibitors against the microsomal triglyceride transfer protein (MTP), HipHop models were generated using 20 known inhibitors from the Binding Database. Using evaluation indicators, the best hypothesis model, Hypo1, was selected and utilised to screen the Traditional Chinese Medicine Database, which resulted in a hit list of 58 drug-like compounds. A homology model of MTP was built by MODELLER and was minimised by CHARMm force field. It was then validated by Ramachandran plot and Verify-3D so as to obtain a stable structure, which was further used to refine the 58 hits using molecular docking studies. Then, five compounds with higher docking scores which satisfied the docking requirements were discovered. Among them, Ginkgetin and Dauricine were most likely to be candidates that exhibition inhibiting effect on MTP. The screening strategy in this study is relatively new to the discovery of MTP inhibitors in medicinal chemistry. Moreover, it is important to note that, lomitapide, an approved MTP inhibitor, fits well with Hypo1 as well as our homology model of MTP, which confirmed the rationality of our studies. The results indicated the applicability of molecular modeling for the discovery of potential natural MTP inhibitors from traditional Chinese herbs.