Computational approaches streamlining drug discovery
Table Of Content
Many molecular docking programs have been developed during recent years, such as, AutoDock [40], Dock [41], FlexX [42], Glide [43], Gold [44], Surflex [45], ICM, and LigandFit [46], and been used successfully in many computer based drug discovery projects. Typically, the major goal of molecular docking is to identify ligands that bind most favorably within receptor binding sites and to determine its most energetically favored binding orientations (poses). A binding pose either refers to a conformation of a ligand molecule within the binding site of its target protein which has been confirmed experimentally, or a computationally modelled hypothetical conformation. The search algorithm and the scoring function are two important components for determining protein-ligand interactions [47]. The search algorithm is responsible for searching different poses and conformations of a ligand within a given target protein and the scoring function estimates the binding affinities of generated poses, ranks them, and identifies the most favorable receptor/ligand binding modes [47, 48].
5. Molecular Dynamic (MD) Simulation
There is a possibility of the development of effective lead molecules against COVID-19 by utilizing natural lead molecules obtained through virtual screening and pharmacokinetic prediction [16]. To speed up the discovery of a potential treatment for SARS-CoV-2 infection in humans, repurposing of broad-spectrum antiviral drugs is a promising strategy due to the availability of the pharmacokinetic and pharmacodynamic data of these drugs [17]. This review article provides useful insights into some of the common in silico methods used in CADD and how these methods have been currently used and can be of help in the drug discovery process of COVID-19. A combination of advanced computational techniques, biological science, and chemical synthesis was introduced to facilitate the discovery process, and this combinational approach enhanced the scale of discovery. Eventually, the term computer-aided drug design (CADD) was adopted for the use of computers in drug discovery [17, 18].
Computational approaches to drug design
Al. recently explored the molecular mechanism of polymyxin E (colistin) resistance in mobile colistin-resistant (mcr-1) bacteria (6). Colistin is the only FDA-approved membrane-active drug to tackle Gram-negative bacteria despite its high toxicity. The simulation results provide clues for the design of new membrane disruptors to treat mcr-1 infections. The advent of fast and practical methods for screening gigascale chemical spaces for drug discovery stimulates further growth of these on-demand spaces, supporting better diversity and the overall quality of identified hits and leads.
6. Protein-protein interaction prediction using SILCS-PPI
Despite the availability of advanced techniques, the structures of a large number of proteins have not been identified [30]. Homology modelling helps in this situation because it can be used to generate the structures of proteins on information available for similar proteins [31]. Following are the steps required to perform a standard MD simulation (see Note 2 for additional MD techniques). Initially applied to discover new chemotypes for cannabinoid receptor CB2 antagonists, V-SYNTHES has shown a hit rate of 23% for submicromolar ligands, which exceeded the hit rate of standard VLS by fivefold, while taking about 100 times less computational resources26.
Availability of data and materials
The concept of duration marks the time entry of drug molecules along with their pharmacological response. The safety rules involve the toxicity parameters which show less or no side effects on the target organisms. Henceforth all the above-mentioned factors contribute equally to the lead optimization of the drug molecule. Upon target validation process, identification of hits and lead discovery phases has to be developed for a novel drug discovery process. Although, physical and biochemical parameters were used to decide the change in the structural property of the compounds to synthesize an effective lead molecule for the development of the drug. Several natural leads have become available in various databases and the literature with biological activity against its specific target, which leads to chemical modification.
Dr. Pellecchia is a Professor of Biomedical Sciences at the School of Medicine of the University of California Riverside (UCR) and is the founding Director of the Center for Molecular and Translational Medicine at UCR. His research laboratory focuses on the design of novel pharmacological tools and therapeutics in oncology, neurodegeneration, and other disease areas. Dr. Rogawski is Professor of Neurology and Pharmacology at the University of California, Davis School of Medicine.
As discussed above, physics-based and data-driven approaches have distinct advantages and limitations in predicting ligand potency. Structure-based docking predictions are naturally generalizable to any target with 3D structures and can be more accurate, especially in eliminating false positives as the main challenge of screening. Conversely, data-driven methods may work in lieu of structures and can be faster, especially with GPU acceleration, although they struggle to generalize beyond data-rich classes of targets. Therefore, there are numerous ongoing efforts to combine physics-based and data-driven approaches in some synergistic ways in general95, and in drug discovery specifically96. An alternative approach proposed to building chemical spaces generates hypothetically synthesizable compounds following simple rules of synthetic feasibility and chemical stability.
Nevertheless, most of the lead compounds undergo structural modification to meet their certain biological properties, so-called lead optimization. However, the investigated lead compound has to satisfy five characteristics to act as a bioactive drug molecule. They are potency, duration, safety, availability, and pharmaceutical acceptability, where potency involves the capability of any molecule to exhibit desirable pharmaceutical properties in smaller quantities. Bioavailability marks the transportation of drug compounds with multiple barriers to reach the target site is called bioavailability.
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Outlook towards computer-driven drug discovery
The initial hits are found by very sensitive methods, such as BIACORE, NMR, X-ray134,135 and potentially cryo-electron microscopy136, to reliably detect weak binding, usually in the 10–100-μM range. The initial screening of the target can be also performed with fragments decorated by a chemical warhead enabling proximity-driven covalent attachment of a low-affinity ligand137. In either case, elaboration of initial fragment hits to full high-affinity ligands is the key bottleneck of fragment-based drug discovery, which requires a major effort involving ‘growing’ the fragment or linking two or more fragments together.
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If researchers used AI in this process at all in recent years, it was primarily to improve existing molecules. This chapter provides an overview on possible approaches to identify active scaffolds (including in silico approximations to approach that task) and computational methods to guide the subsequent optimization process. 6When constructing the final list of compound for experimental assays from VS, in addition to the binding score, drug likeness can be another criterion to further filter the list.
The results obtained showed inhibitors had low cytotoxicities, suggesting potential for drug development [82]. Inhibition of these two enzymes constitutes treatment for neurological diseases like autism spectral disorder, Alzheimer, and fragile X syndrome. Alokam et al. reported the successful use of CADD to design dual inhibitors for these enzymes [76], by employing a combination of pharmacophores and using a molecular docking approach to identify chemical entities.
Although allowing comprehensive space coverage, the reaction path and success rate of generated compounds are unknown, and thus require computational prediction of their practical synthesizability. More recently, virtual on-demand chemical databases (fully enumerated) and spaces (not enumerated) allow fast parallel synthesis from available building blocks, using validated or optimized protocols, with synthetic success of more than 80% and delivery in 2–3 weeks (see the figure, part b). The virtual chemical spaces assure high chemical novelty and allow fast polynomial growth with the addition of new synthons and reaction scaffolds, including 4+ component reactions. Examples include Enamine REAL, Galaxy by WuXi, CHEMriya by Otava and private databases and spaces at pharmaceutical companies. Research suggests inhibition of this enzyme stops the production of β-amyloid, and thus, prevents NDs like Alzheimer's disease [68].
The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity. "It's a real breakthrough for drug discovery," says Gisbert Schneider, Professor at ETH Zurich's Department of Chemistry and Applied Biosciences. Together with his former doctoral student Kenneth Atz, he has developed an algorithm that uses artificial intelligence (AI) to design new active pharmaceutical ingredients.
The ML workflow was verified to be able to generate models not only capable of predicting resistance profiles but also identifying the responsible genes. Al. conducted an antibiotic activity assay screen of near 2,300 chemically diverse FDA approved and natural product compounds targeting E. Deep neural network-based DL models were then trained to predict the inhibition probabilities from the chemical structures and properties of tested compounds alone.
Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics. To speed up virtual screening of ultra-large chemical libraries, several groups have suggested hybrid iterative approaches, in which results of structure-based docking of a sparse library subset are used to train ML models, which are then used to filter the whole library to further reduce its size. These methods, including MolPal25, Active Learning110 and DeepDocking111, report as much as 14–100 reduction in the computational cost for libraries of 1.4 billion compounds, although it is not clear how they would scale to rapidly growing chemical spaces. In virtual screening approaches, a synergetic use of physics-based docking with data-based scoring functions may be highly beneficial.
Between 2009 and 2018 the average cost of drug design and discovery was reported to be up to $2.8 billion [1]. Nevertheless, current drug design also takes into consideration other relevant processes than influence drug efficacy and safety (e.g., bioavailability, metabolic stability, interaction with antitargets). A pharmacophore model is defined as spatially distributed chemical features that are essential for specific ligand-target binding. It represents a simplification of the detailed energetic information used by docking methods and so its computational requirements are much lower. While multiple methods can be used to generate pharmacophores (84), we will present a method based on information from SILCS as described in section 3.2. Besides efforts to develop small molecule antibiotics to counteract the evolving drug resistance of bacteria, researchers are also applying biologics-based drugs such as monoclonal antibodies (mAbs) in the battle (137–140).
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