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

Computational and micro-analytical techniques to study the in vitro and in silico models of novel therapeutic drugs

Submitted in fulfillment of the requirements for the Doctor of Philosophy degree in Chemistry, Durban University of Technology, Durban, South Africa, 2016.In drug discovery and development projects, metabolism of new chemical entities (NCEs) is a major contributing factor for the withdrawal of drug candidates, a major concern for other chemical industries where chemical-biological interactions are involved. NCEs interact with a target macro-molecule to stimulate a pharmacological or toxic response, known as pharmacodynamics (PD) effect or through the Adsorption, Distribution, Metabolism, and Excretion (ADME) process, triggered when a bio-macromolecule interacts with a therapeutic drug. Therefore, the drug discovery process is important because 75% of diseases known to human kind are not all cured by therapeutics currently available in the market. This is attributed to the lack of knowledge of the function of targets and their therapeutic use in order to design therapeutics that would trigger their pharmacological responses. Accordingly, the focus of this work is to develop cost saving strategies for medicinal chemists involved with drug discovery projects. Therefore, studying the synergy between in silico and in vitro approaches maybe useful in the discovery of novel therapeutic compounds and their biological activities. In this work, in silico methods such as structure-based and ligand-based approaches were used in the design of the pharmacophore model, database screening and flexible docking methods. Specifically, this work is presented by the following case studies: The first involved molecular docking studies to predict the binding modes of catechin enantiomer to human serum albumin (HSA) interaction; the second involved the use of docking methods to predict the binding affinities and enantioselectivity of the interaction of warfarin enantiomers to HSA. the third case study involved a combined computational strategy in order to generate information on a diverse set of steroidal and non-steroidal CYP17A1 inhibitors obtained from literature with known experimental IC50 values. Finally, the fourth case study involved the prediction of the site of metabolisms (SOMs) of probe substrates to Cytochrome P450 metabolic enzymes CYP 3A4, 2D6, and 2C9 making use of P450 module from Schrödinger suite for ADME/Tox prediction. The results of case study I were promising as they were able to provide clues to the factors that drive the synergy between experimental kinetic parameters and computational thermodynamics parameters to explain the interaction between drug enantiomers and thetarget protein. These parameters were correlated/converted and used to estimate the pseudo enantioselectivity of catechin enantiomer to HSA. This approach of combining docking methodology with docking post-processing methods such as MM-GBSA proved to be vital in estimating the correct pseudo binding affinities of a protein-ligand complexes. The enantioselectivity for enantiomers of catechin to HSA were 1,60 and 1,25 for site I and site II respectively. The results of case study II validates and verifies the preparation of ligands and accounting for tautomers at physiological pH, as well as conformational changes prior to and during docking with a flexible protein. The log KS = 5.43 and log KR = 5.34 for warfarin enantiomer-HSA interaction and the enantioselectivity (ES = KS/KR) of 1.23 were close to the experimental results and hence referred to as experimental-like affinity constants which validated and verified their applicability to predict protein-ligand binding affinities. In case study III, a 3D-QSAR pharmacophore model was developed by using 98 known CYP17A1 inhibitors from the literature with known experimental IC50 values. The starting compounds were diverse which included steroidal and non-steroidal inhibitors. The resulting pharmacophore models were trained with 69 molecules and 19 test set ligands. The best pharmacophore models were selected based on the regression coefficient for a best fit model with R2 (ranging from 0.85-0.99) & Q2 (ranging from 0.80-0.99) for both the training and test sets respectively, using Partial Least Squares (PLS) regression. On the other hand, the best pharmacophore model selected was further used for a database screening of novel inhibitors and the prediction of their CYP17A1 inhibition. The hits obtained from the database searches were further subjected to a virtual screening workflow docked to CYP17A1 enzyme in order to predict the binding mode and their binding affinities. The resulting poses from the virtual screening workflow were subjected to Induced Fit Docking workflow to account for protein flexibility during docking. The resulting docking poses were examined and ranked ordered according to the docking scores (a measure of affinity). Finally, the resulting hits designed from an updated model from case study III were further synthesized in an external organic chemistry laboratory and the synthetic protocols as well as spectroscopic data for structure elucidation forms part of the provisional patent specification. A provisional patent specification has been filed (RSA Pat. Appln. 2015/ 07849). The case studies performed in this thesis have enabled the discovery of non-steroidal CYP17A1 inhibitors.

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