Abstract
Since Human Genome Project (HGP) revealed the heterogeneity of individuals, precision medicine that proposes the customized healthcare has become an intractable and hot research. Meanwhile, as the Precision Medicine Initiative launched, precision drug design which aims at maximizing therapeutic effects while minimizing undesired side effects for an individual patient has entered a new stage. One of the key strategies of precision drug design is target based drug design. Once a key pathogenic target is identified, rational drug design which constitutes the major part of precision drug design can be performed. Examples of rational drug design on novel druggable targets and protein–protein interaction surfaces are summarized in this review. Besides, various kinds of computational modeling and simulation approaches increasingly benefit for the drug discovery progress. Molecular dynamic simulation, drug target prediction and in silico clinical trials are discussed. Moreover, due to the powerful ability in handling high-dimensional data and complex system, deep learning has efficiently promoted the applications of artificial intelligence in drug discovery and design. In this review, deep learning methods that tailor to precision drug design are carefully discussed. When a drug molecule is discovered, the development of specific targeted drug delivery system becomes another key aspect of precision drug design. Therefore, state-of-the-art techniques of drug delivery system including antibody-drug conjugates (ADCs), and ligand-targeted conjugates are also included in this review.
Highlights
In January 2015, President Obama announced the new Precision Medicine Initiative1 to bring human closer to cure diseases like cancer and diabetes and to give all of us the ability to access to the personalized information to keep ourselves and our families in good health
With the exponential growth of the therapeutically relevant target structures being deposited in Protein Data Bank (PDB), precision drug design has entered a new era
In order to simultaneously occupy the ATP binding site and rapamycin-binding site, a bivalent mTOR inhibitor which contained rapamycin, a linker and MLN0128 (23 in Figure 7A, an mTOR kinase inhibitor, currently in clinical trials) was designed and synthesized. This compound could potently inhibit tumor growth and mTOR signaling in wild-type mTOR-expressing cells as well as in cells that have acquired resistance to rapalogs or ATP-competitive inhibitors, or both (Rodrik-Outmezguine et al, 2016)
Summary
Chen Wang1,2,3†, Pan Xu2,3†, Luyu Zhang, Jing Huang, Kongkai Zhu1* and Cheng Luo2,3*. One of the key strategies of precision drug design is target based drug design. Once a key pathogenic target is identified, rational drug design which constitutes the major part of precision drug design can be performed. Examples of rational drug design on novel druggable targets and protein–protein interaction surfaces are summarized in this review. Due to the powerful ability in handling high-dimensional data and complex system, deep learning has efficiently promoted the applications of artificial intelligence in drug discovery and design. Deep learning methods that tailor to precision drug design are carefully discussed. When a drug molecule is discovered, the development of specific targeted drug delivery system becomes another key aspect of precision drug design.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
