The concept of computer-aided drug discovery 3 was developed in the 1970s and popularized by Fortune magazine in 1981, and has since been through several cycles of hype and disillusionment 4. Finding fast and accessible ways to discover more diverse pools of higher-quality chemical probes, hits and leads with optimal absorption, distribution, metabolism, excretion and toxicology (ADMET) and pharmacokinetics (PK) profiles at the early stages of DDD would improve outcomes in preclinical and clinical studies and facilitate more effective, accessible and safer drugs. Moreover, the high failure rate in clinical trials (currently 90%) 2 is largely explained by issues rooted in early discovery such as inadequate target validation or suboptimal ligand properties. Preclinical efforts themselves account for more than 43% of expenses in pharma, in addition to major public funding 1, driven by the high attrition rate at every step from target selection to hit identification and lead optimization to the selection of clinical candidates. Although it is accepted that clinical studies are the priciest part of the development of each drug, most time-saving and cost-saving opportunities reside in the earlier discovery and preclinical stages. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.ĭespite amazing progress in basic life sciences and biotechnology, drug discovery and development (DDD) remain slow and expensive, taking on average approximately 15 years and approximately US$2 billion to make a small-molecule drug 1. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches.
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Taking full advantage of these resources requires fast computational methods for effective ligand screening. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma.
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