Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Pharmacophore Model-Based Virtual Screening Workflow for Discovery of Inhibitors Targeting <i>Plasmodium falciparum</i> Hsp90

Plasmodium falciparum causes the most lethal and widespread form of malaria. Eradication of malaria remains a priority due to the increasing number of cases of drug resistance. The heat shock protein 90 of P. falciparum (PfHsp90) is a validated drug target essential for parasite survival. Most PfHsp90 inhibitors bind at the ATP binding pocket found in its N-terminal domain, abolishing the chaperone's activities, which leads to parasite death. The challenge is that the NTD of PfHsp90 is highly conserved, and its disruption requires selective inhibitors that can act without causing off-target human Hsp90 activities. We endeavored to discover selective inhibitors of PfHsp90 using pharmacophore modeling, virtual screening protocols, induced fit docking (IFD), and cell-based and biochemical assays. The pharmacophore model (DHHRR), composed of one hydrogen bond donor, two hydrophobic groups, and two aromatic rings, was used to mine commercial databases for initial hits, which were rescored to 20 potential hits using IFD. Eight of these compounds displayed moderate to high activity toward P. falciparum NF54 (i.e., IC50s ranging from 6.0 to 0.14 μM) and averaged >10 in terms of selectivity indices toward CHO and HepG2 cells. Additionally, four compounds inhibited PfHsp90 with greater selectivity than a known inhibitor, harmine, and bound to PfHsp90 with weak to moderate affinity. Our findings support the use of a pharmacophore model to discover diverse chemical scaffolds such as FM2, FM6, F10, and F11 exhibiting anti-Plasmodium activities and serving as valuable new PfHsp90 inhibitors. Optimization of these hits may enable their development into potent leads for future antimalarial drugs