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Rational drug design
Rational drug design is a targeted, structure-based approach in pharmaceutical development that creates 🆕 medications by analyzing the 3D structure and function of biological targets (e.g., proteins, enzymes) rather than relying on trial-and-error. It uses computational modeling (CADD), NMR, and X-ray crystallography to design molecules that specifically bind to and modulate these targets, reducing side effects and speeding up discovery.
More specifically, traditional drug discovery—known also as forward pharmacology—relies on high-throughput screening of chemical libraries to match a certain cellular modulation to a specific drug treatment. The opposite of this is the rational drug design—also called reverse pharmacology or just drug design—which is based on the hypothesis that a “designed” molecule can induce a specific modulation of a biological target. In other words, drug design 🎨🖌️ is the process of finding drug candidates during drug discovery based on the knowledge of a biological target, and involves the design of molecules that complement the shape and charge of a target—most commonly a protein or a nucleic acid—in order to interact, bind and modulate the target in a way that produces therapeutic value. From an experimental point of view, both traditional drug discovery and rational drug design are equally important, so the pharmaceutical industry depends on the combined efforts of both of them.
When it comes to rational drug design—for a biomolecule to be selected as a target—knowledge is required, ➡️ knowledge that the modulation of the selected target will be disease-modifying, and ➡️ knowledge that the target is druggable.
Since drug design frequently relies on computer modeling techniques—that is the representation of a 3D structure of chemical and biological molecules—is also referred to as computer-aided drug design. Moreover, the drug design based on the knowledge of the 3D structure of a biological target is known as structure-based drug design (SBDD).
Accordingly, the rational drug design (RDD) companies that utilize knowledge of biological targets to engineer molecules that specifically bind to them, often rely heavily on 👉AI, 👉simulation, and 👉structural biology (like X-ray crystallography or Cryo-EM). This approach aims to reduce the time and cost compared to traditional trial-and-error chemistry.
For a more detailed analysis of computer-aided drug design and structure-based drug design (SBDD) techniques👇
Examples of specialized Structure-Based Design companies are: Vertex Pharmaceuticals (USA): A pioneer in rational drug design, famously using it for cystic fibrosis therapies (Just – Evotec Biologics Receives Grant for AI-Driven Optimization of Monoclonal Antibody Developability for Affordable Access); Evotec (Germany): Uses its “EVOrationale” platform for structure-guided lead optimization (Just – Evotec Biologics Receives Grant for AI-Driven Optimization of Monoclonal Antibody Developability for Affordable Access). Meddenovo Drug Design (France): Focuses on AI-supported rational design of cyclic peptides (Mexa Design); Ventus Therapeutics (USA): Employs “ReSOLVE”, a platform focusing on structural biology and computational chemistry for immunology and neurology (ReSOLVE® combines the latest advances in AI/ML, protein science, structural biology, and biophysics, to substantially increase the speed and accuracy of small-molecule drug discovery); Cresset (UK): Provides software and services for in silico molecule discovery, design, and optimization (AI unlocks unparalleled efficiency gains in Flare™ V11, Cresset’s leading software solution for digital molecular discovery); Nostrum Biodiscovery (Spain): Uses AI, quantum mechanics, and molecular dynamics for drug design (PELE Loop Closure); and CrystalsFirst (Germany): Specializes in structural biology, specifically crystal-based screening, to guide rational drug design (CRYSTALSFIRST TO EXCLUSIVELY OFFER NEXMRʼS ULTRAFAST NMR SCREENING TECHNOLOGY FOR FRAGMENT-BASED DRUG DISCOVERY).
While some emerging and niche rational design companies are the following: Polaris Quantum Biotech (USA): Uses quantum computers for drug design, finding leads within a 10³⁰ chemical space (PolarisQB Published A Manuscript Demonstrating The Superiority Of Quantum Computing Over AI In Drug Design); PioLigOn (Poland): Focuses on computational de novo peptide design, significantly reducing the need for wet-lab synthesis (VPDesigner42™ is a powerful drug discovery platform leveraging the latest advances in computational de novo peptide design and unnatural peptide synthesis); and Atomic AI (USA): Leverages AI and structural biology to innovate in RNA drug discovery.
