During the past 40 years, the increase in computational power and the availability of chemo informatics data have allowed computational chemistry methods to become a vital tool for drug discovery services. Conventional drug discovery is a very time-consuming and costly process with a low turnover rate of druggable new chemical entities. It requires a collective effort of multiple disciplines to discover novel and effective drugs. In this direction, computer aided drug designing (CADD), involving various computational approaches assist in rationalizing the drug discovery and development process. It uses state-of-art technologies to improve the drug discovery workflow in a cost and time-effective manner. These technologies deliver possible therapeutic solutions by aligning chemical and biological spaces in drug discovery programs. It employs a variety of techniques viz. molecular docking, pharmacophore mapping, homology modeling, quantitative structure activity relationship (QSAR), molecular dynamics simulations, high throughput virtual screening (HTVS), pharmacokinetic-pharmacodynamics (PKPD) modeling, drug metabolism, and pharmacokinetics (DMPK) and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, molecular mechanics and quantum mechanics.
In the post-genomic era, owing to an increase of small molecule and macromolecule information, computational approaches have been applied in almost every stage of drug development – from identifying targets, lead identification, optimization, preclinical data generation, and drug development to formulation. This greatly accelerates the drug discovery process by reducing the number of iterations/cycles required. CADD has successful cases to its credit in the discovery of drugs such as Nelfinavir (HIV-1 protease inhibitors), Captopril (Angiotensin converting enzyme inhibitor), Aliskiren (Renin inhibitor), Dorzolamide (Carbonic anhydrase inhibitor), Zanamivir (Neuraminidase inhibitor), and Imatinib (Tyrosine-kinase inhibitor). These successful studies clearly indicate that CADD provides practical and realistic ways of helping medicinal chemists and biologists to address their goals of discovering novel active and lead-like molecules while eliminating ones that are inactive, reactive, and/or toxic.
Why Computer-Aided Drug Designing Is So Important?
It takes 10-15 years of huge efforts along with an approximate cost of $800 million to $1.8 billion in drug discovery and developments for a single drug candidate. In this context, CADD is rapidly imbibing steam at integrated drug discovery services due to its tremendous foreseen potential to curb costly drug discovery processes in terms of money, manpower, and time. CADD is capable of producing the hits against novel drug targets with/without 3D structure at a plausible rate in a time-efficient way as it uses a focused protocol than traditional high throughput screening (HTS) and combinatorial chemistry.
The traditional HTS assays often need extensive validation and development before they can be used. Also, this process requires huge time as well as money. However, CADD requires significantly less preparation time, scientists can perform CADD studies while preparing the traditional HTS assays. Further, combining both tools can be used in parallel provides an additional benefit for CADD in a drug discovery project. CADD has the capability to significantly decrease the number of compounds necessary to prioritize and screen using various techniques viz. docking, pharmacophore mapping, QSAR leading to reduced cost and workload of a full HTS screen without compromising lead identification and discovery.
Why CADD Is Useful For Drug Discovery Services?
In a drug discovery operation, CADD is generally used for three major purposes: (1) to filter large compound libraries into smaller subsets with predicted activities for biological testing; (2) to optimize lead compounds to favorable pharmacokinetics and pharmacodynamics properties; (3) to design novel compounds by fragment or de novo design approach. These approaches are very general and often used in drug discovery.
Types of Computer-aided Drug Design Approaches
CADD approaches are categorized into structure-based drug design (SBDD) and ligand-based drug design (LBDD).
SBDD approach uses the 3D structure of the target (enzyme/receptor) for identifying/screening of potential hits, followed by optimization, synthesis, biological testing. SBDD can be divided into two categories – De novo approach and the virtual screening approach. De novo drug design exploits information from the 3D receptor to ﬁnd small fragments that complement well with the binding site. On the other hand, virtual screening uses available compound libraries to identify hits with speciﬁc bioactivity. Some of the underlying in silico techniques is given below:
Homology Modeling – An integrated drug discovery agency can use this method to predict the 3D structure of the target protein based on its homolog protein structure available in the protein data bank.
Molecular Docking – It is one of the most well-known SBDD methods that predict possible binding modes of a compound in the binding site of target and estimates afﬁnity based on its conformation and complementarity.
Molecular Dynamics Simulations – This method calculates the trajectory of a system by the application of molecular mechanics.
Structure-Based HTVS – This method is used for identifying putative hits out of hundreds of thousands of compounds to targets of known structure.
LBDD subjects a collection of structurally diverse molecules with known potency to computational modeling for developing theoretical predictive models. These models are used for structural optimization to enhance potency and improve the physicochemical property.
Some of the underlying in silico techniques is given below –
QSAR – QSAR studies are based on the premise that changes in bioactivity are associated with molecular structural variations in a set of compounds.
Pharmacophore Modeling – Pharmacophore screening aims to identify compounds containing different scaffolds, but with a similar 3D arrangement of key interacting functional groups.
Shape-based HTVS – Shape of potential hit or lead compound used to retrieve similar but structurally diverse compound applying Tanimoto coefficient as a measure for shape similarity
Over time, both approaches continued to improve and evolve discretely. Though, amalgamating SBDD and LBDD strategies have been recognized to be more effective than any single approach in drug discovery.
Get Computer-Aided Drug Designing Services From Integral BioSciences
It must be clear to readers that CADD can expedite the early stages of therapeutic discovery by speeding up processes like hit identification. However, it is essential for pharma and biotech companies to collaborate with agencies with sufficient expertise in molecular modeling. Integral BioSciences is a reputed contract research organization in India with an impeccable record. The agency has helped various pharmaceutical enterprises by providing focused assistance in preclinical research. It provides highly experienced teams of scientists with proficiency in structure-based drug design, ligand-based drug design, QSAR modeling, protein modeling, virtual screening, etc. The CRO uses state-of-the-art software and hardware to provide CADD services.
Connect with Integral BioSciences for efficient computer-aided drug designing services by experts.
References for further reading-
Sliwoski et al., Pharmacol Rev. (2014), 66(1): 334-359
Macalino et al., Arch. Pharm. Res. (2015), 38(9):1686-701
Taft et al., J Pharm Sci. (2008), 97(3):1089-98