What is Structure-Based Drug Discovery?

Drug discovery aims to identify safe, efficacious, and effective compounds in the treatment of diseases. The drug development process, comprising the discovery, development, clinical trial, and registry phases, is time-consuming, resource-intensive, and costly. Researchers in academia and industry, including contract research organizations (CROs), constantly work on developing new methods to increase the efficiency of drug discovery and development.

Computer-aided drug design (CADD), for example, employs molecular modeling techniques which enable the simulation and prediction of factors like toxicity, activity, bioavailability, and efficacy of compounds even before in vitro testing. As a result, better planning and direction of research lead to improved efficiency through a decrease in unnecessary experiments, run time, and overall research costs. CADD started at a US-based global CRO in 1997 and has since delivered new chemical compounds and contributed to successful drug discovery outcomes. Two types of CADD are structure-based drug design (SBDD) and ligand-based drug design. In this article, we will focus on structure-based drug discovery, a computational technique used extensively by the drug discovery and development industry and scientists.

Structure-Based Drug Discovery

Drug discovery was traditionally performed through random screening and observations of natural products’ effects on known diseases. Today, structural and molecular biology and biomolecular spectroscopic structure determination methods contribute to faster and more cost-efficient lead discovery than traditional drug discovery methods.

SBBD focuses on the three-dimensional (3D) structure of target proteins and disease at the molecular level, and the availability of 3D structures of over 100,000 proteins provides the foundation for SBDD. It is an iterative process with multiple cycles run to bring the optimized drug candidate to clinical trial:

First phase

Identification of potential therapeutic targets and active ligands. Computer algorithms are used to dock massive databases of small molecules or fragments of compounds into the binding cavity of the target protein.

Second phase

Synthesis and optimization of top hits. The 3D structure of the target protein in complex with the promising ligand is determined. Top-ranked compounds are tested in vitro in biochemical assays.

Third phase

Optimized lead compounds enter preclinical and clinical trials to be approved by regulatory bodies.

Fourth phase

Compounds that pass the clinical trials are ready to be distributed for clinical use.

Computational techniques needed to process the “big data” generated include:

  • Structure-based virtual screening (SBVS) which encompasses methods that exploit the 3D structure of the target
  • Molecular docking, a silico structure-based method first developed in the 1980s, is an important component of drug discovery applied in both industrial and academic settings. It enables the identification of novel compounds without knowing the chemical structure of other target modulators.
  • Molecular dynamics (MD) simulations play a role in studying biological systems and provide researchers with structural information less easily captured by current experimental methods. Tech advances allow researchers to run long-time-scale simulations to gather more detailed data.

Structure-based (SB) methods have been integrated with artificial intelligence/machine learning (AI/ML) in oncology drug discovery strategies. ML-SB integration methods apply to different use case scenarios (not limited to cancer drugs), including ML-based scoring functions for SB applications.

For SBDD against SARS-CoV-2, deep learning (DL) technology and DL-based molecular dynamics simulation approaches were used to address problems of protein structure and binding prediction, drug virtual screening, molecular docking, and complex evolution.

Researchers have also studied the application of quantum mechanics in structure-based drug discovery, citing examples in the prediction of protein-ligand geometries, protein-ligand binding affinities, and ligand strain on binding.

Structure-Based Drug Discovery Outcomes


More than two decades ago, a study of the SBDD process revealed that the automation of all steps shortened the timeline. Since then, supercomputers, computer clusters, cloud computing, and machine learning (ML) methods have further sped up lead identification, evaluation, and big-data handling.

Successes in drug discovery

SBDD has identified numerous drugs, including HIV-1-inhibiting FDA-approved drugs, a thymidylate synthase inhibitor (raltitrexed), a potent inhibitor of HIV protease discovered by protein modeling and MD simulation (amprenavir), and the antibiotic norfloxacin.

Drugs that came to market with the assistance of virtual screening include antihypertensive drugs (aliskiren and captopril), drugs for the treatment of human immunodeficiency virus (saquinavir, ritonavir, and indinavir), fibrinogen antagonists (tirofiban), glaucoma treatment (dorzolamide), a selective antiviral for influenza (zanamivir), and protease inhibitor for the treatment of hepatitis C (boceprevir). SBBD has also been applied to an increasing number of G protein-coupled receptor (GPCR) targets with several candidates in clinical trials.

Ongoing Work in Structure-Based Drug Discovery

Researchers recognize that molecular dynamics (MD) can overcome limitations routinely appearing in ligand docking calculations without sampling the protein conformational rearrangements during ligand binding. Other challenges faced in drug discovery require improvement in the efficacy of virtual screening methods, computational chemogenomic studies, quality and the number of computational web sources, the structure of multitarget drugs, and algorithms for toxicity prediction. In addition, efforts to collaborate with related fields of study are expected to enhance lead identification and optimization.

Furthermore, contract research organizations (CROs) like Vial can help sponsors take on the challenges of running clinical trials. Vial CRO can help sponsors by providing a range of CRO services that support efficient and effective trial management. These may include site selection, patient recruitment, data management, statistical analysis, and regulatory compliance. By partnering with a CRO like Vial, sponsors can leverage the expertise of experienced professionals and access the latest technologies and best practices in clinical trial management. Contact a team member to learn how Vial can help streamline the drug development process and increase the chances of success for new therapies.

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