Recent applications of computational methods to allosteric drug discovery

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function. Here we examine the extent to which structure-based drug design is impacting allosteric drug design by highlighting several targets across a variety of target classes.

Ligand-Based and Docking-Based Virtual Screening of MDM2 Inhibitors as Potent Anticancer Agents

A ligand-based and docking-based virtual screening was carried out to identify novel MDM2 inhibitors. A pharmacophore model with four features was used for virtual screening, followed by molecular docking. Seventeen compounds were selected for an in vitro MDM2 inhibition assay, and compounds AO-476/43250177, AG-690/37072075, AK-968/15254441, AO-022/43452814, and AF-399/25108021 showed promising MDM2 inhibition activities with K_i values of 9.5, 8.5, 23.4, 3.2, and 23.1 μM, respectively. Four compounds also showed antiproliferative activity, and compound AO-022/43452814 was the most potent hit with IC₅₀ values of 19.35, 26.73, 12.63, and 24.14 μM against MCF7 (p53 +/+), MCF7 (p53 -/-), HCT116 (p53 +/+), and HCT116 (p53 -/-) cell lines, respectively. Compound AO-022/43452814 could be used as a scaffold for the development of anticancer agents targeting MDM2.

Inhibitory effect of cathepsin K inhibitor (ODN-MK-0822) on invasion, migration and adhesion of human breast cancer cells in vitro

Approximately 90% of patients with advanced breast cancer develop bone metastases; an event that results in severe decrease of quality of life and a drastic deterioration in prognosis. Therefore, to increase the survival of breast cancer patients, the development of new therapeutic strategies to impair metastatic process and skeletal complications is critical. Previous studies on the role of cathepsin K (CTSK) in metastatic spreading led to several strategies for inhibition of this molecule such as MIV-711 (Medivir), balicatib and odanacatib (ODN) which were on trial in the past. The present study intended to assess the anti-metastatic efficacy of ODN in breast cancer cells. Human breast cancer cell lines MDA-MB-231 were treated with different concentrations of ODN and performed invasion, adhesion and migration assays and, RT-PCR and western blot to evaluate the effect of ODN on the metastatic potential of breast cancer cells. ODN markedly decreased wound healing cell migration, invasion and adhesion at a dose dependent manner. ODN inhibits cell invasion by decreasing the matrix metalloproteinase (MMP-9) with the upregulation of TIMP-1 expression. ODN effectively inhibited the phosphorylation of extracellular signal-regulated kinase (ERK), p38, and c-Jun N-terminal Kinase (JNK), and blocked the expression of β-integrins and FAK proteins. ODN also significantly inhibited PI3K downstream targets Rac1, Cdc42, paxillin and Src which are critical for cell adhesion, migration and cytoskeletal reorganization. ODN exerts anti-metastatic action through inhibition of signaling pathway for MMP-9, PI3K and MAPK. This indicates potential therapeutic effects of ODN in the treatment of metastatic breast cancer.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.