Artificial intelligence and big data facilitated targeted drug discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data.The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer.DrugBank is a database including detailed information of approved,investigational and withdrawn drugs,as well as other nutraceutical and metabolite structures.PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds.Protein Data Bank is a crystal structure database including X-ray,cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands.On the other hand,artificial intelligence(AI)is playing an important role in the drug discovery progress.The integration of such big data and AI is making a great difference in the discovery of novel targeted drug.In this review,we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption,distribution,metabolism,excretion and toxicity properties.

A novel inhibitor of ARfl and ARv7 induces protein degradation to overcome enzalutamide resistance in advanced prostate cancer

Enzalutamide(ENZ) is a second-generation androgen receptor(AR) antagonist used for the treatment of castration-resistant prostate cancer(CRPC) and reportedly prolongs survival time within a year of starting therapy. However, CRPC patients can develop ENZ resistance(ENZR), mainly driven by abnormal reactivation of AR signaling, involving increased expression of the full-length AR(ARfl)or dominantly active androgen receptor splice variant 7(ARv7) and ARfl/ARv7 heterodimers. There is currently no efficient treatment for ENZR in CRPC. Herein, a small molecule LLU-206 was rationally designed based on the ENZ structure and exhibited potent inhibition of both ARfl and constitutively active ARv7 to inhibit PCa proliferation and suppress ENZR in CRPC. Mechanically, LLU-206 promoted ARfl/ARv7 protein degradation and decreased ARfl/ARv7 heterodimers through mouse double minute 2-mediated ubiquitination. Finally, LLU-206 exhibited favorable pharmacokinetic properties with poor permeability across the blood-brain barrier, leading to a lower prevalence of adverse effects, including seizure and neurotoxicity, than ENZ-based therapies. In a nutshell, our findings demonstrated that LLU-206 could effectively inhibit ARfl/ARv7-driven CRPC by dual-targeting of ARfl/ARv7 heterodimers and protein degradation, providing new insights for the design of new-generation AR inhibitors to overcome ARfl/ARv7-driven CRPC.

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

The discovery of targeted drugs heavily relies on three-dimensional(3D)structures of target proteins.When the 3D structure of a protein target is unknown,it is very difficult to design its corresponding targeted drugs.Although the 3D structures of some proteins(the so-called undruggable targets)are known,their targeted drugs are still absent.As increasing crystal/cryogenicelectron microscopy structures are deposited in Protein Data Bank,it is much more possible to discover the targeted drugs.Moreover,it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites.In this review,we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.