Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning.

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II.

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Indole-Containing Amidinohydrazones as Nonpeptide, Dual RXFP3/4 Agonists: Synthesis, Structure-Activity Relationship, and Molecular Modeling Studies.

The central relaxin-3/RXFP3 system plays important roles in stress responses, feeding, and motivation for reward. However, exploration of its therapeutic applications has been hampered by the lack of small molecule ligands and the cross-activation of RXFP1 in the brain and RXFP4 in the periphery. Herein, we report the first structure-activity relationship studies of a series of novel nonpeptide amidinohydrazone-based agonists, which were characterized by RXFP3 functional and radioligand binding assays. Several potent and efficacious RXFP3 agonists (e.g., 10d) were identified with EC50 values <10 nM. These compounds also had high potency at RXFP4 but no agonist activity at RXFP1, demonstrating > 100-fold selectivity for RXFP3/4 over RXFP1. In vitro ADME and pharmacokinetic assessments revealed that the amidinohydrazone derivatives may have limited brain permeability. Collectively, our findings provide the basis for further optimization of lead compounds to develop a suitable agonist to probe RXFP3 functions in the brain.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates.

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Synthesis, Molecular Pharmacology, and Structure-Activity Relationships of 3-(Indanoyl)indoles as Selective Cannabinoid Type 2 Receptor Antagonists.

Synthetic indole cannabinoids characterized by a 2',2'-dimethylindan-5'-oyl group at the indole C3 position constitute a new class of ligands possessing high affinity for human CB2 receptors at a nanomolar concentration and a good selectivity index. Starting from the neutral antagonist 4, the effects of indole core modification on the pharmacodynamic profile of the ligands were investigated. Several N1 side chains afforded potent and CB2-selective neutral antagonists, notably derivatives 26 (R1 = n-propyl, R2 = H) and 35 (R1 = 4-pentynyl, R2 = H). Addition of a methyl group at C2 improved the selectivity for the CB2 receptor. Moreover, C2 indole substitution may control the CB2 activity as shown by the functionality switch in 35 (antagonist) and 49 (R1 = 4-pentynyl, R2 = CH3, partial agonist).

Binding Modes and Selectivity of Cannabinoid 1 (CB1) and Cannabinoid 2 (CB2) Receptor Ligands.

The cannabinoid (CB) receptors (CB1R and CB2R) represent a promising therapeutic target for several indications such as nociception and obesity. The ligands with nonselectivity can be traced to the high similarity in the binding sites of both cannabinoid receptors. Therefore, the need for selectivity, potency, and G-protein coupling bias has further complicated the design of desired compounds. The bias of currently studied cannabinoid agonists is seldom investigated, and agonists that do exhibit bias are typically nonselective. However, certain long-chain endocannabinoids represent a class of selective and potent CB1R agonists. The binding mode for this class of compounds has remained elusive, limiting the implementation of its binding features to currently studied agonists. Hence, in the present study, the binding poses for these long-chain cannabinoids, along with other interesting ligands, with the receptors have been determined, by using a combination of molecular docking and molecular dynamics (MD) simulations along with molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The binding poses for the long-chain cannabinoids implicate that a site surrounded by the transmembrane (TM)2, TM7, and extracellular loop (ECL)2 is vital for providing the long-chain ligands with the selectivity for CB1R, especially I267 of CB1R (corresponding to L182 of CB2R). Based on the obtained binding modes, the calculated relative binding free energies and selectivity are all in good agreement with the corresponding experimental data, suggesting that the determined binding poses are reasonable. The computational strategy used in this study may also prove fruitful in applications with other GPCRs or membrane-bound proteins.

Pictet-Spengler condensations using 4-(2-aminoethyl)coumarins.

Androgen-deprivation therapy (ADT) is only a palliative measure, and prostate cancer invariably recurs in a lethal, castration-resistant form (CRPC). Prostate cancer resists ADT by metabolizing weak, adrenal androgens to growth-promoting 5α-dihydrotestosterone (DHT), the preferred ligand for the androgen receptor (AR). Developing small-molecule inhibitors for the final steps in androgen metabolic pathways that utilize 17-oxidoreductases required probes that possess fluorescent groups at C-3 and intact, naturally occurring functionality at C-17. Application of the Pictet-Spengler condensation to substituted 4-(2-aminoethyl)coumarins and 5α-androstane-3-ones furnished spirocyclic, fluorescent androgens at the desired C-3 position. Condensations required the presence of activating C-7 amino or N,N-dialkylamino groups in the 4-(2-aminoethyl)coumarin component of these condensation reactions. Successful Pictet-Spengler condensation, for example, of DHT with 9-(2-aminoethyl)-2,3,6,7-tetrahydro-1H,5H,11H-pyrano[2,3-f]pyrido[3,2,1-ij]quinolin-11-one led to a spirocyclic androgen, (3R,5S,10S,13S,17S)-17-hydroxy-10,13-dimethyl-1,2,2',3',4,5,6,7,8,8',9,9',10,11,12,12',13,13',14,15,16,17-docosahydro-7'H,11'H-spiro-[cyclopenta[a]phenanthrene-3,4'-pyrido[3,2,1-ij]pyrido[4',3':4,5]pyrano[2,3-f]quinolin]-5'(1'H)-one. Computational modeling supported the surrogacy of the C-3 fluorescent DHT analog as a tool to study 17-oxidoreductases for intracrine, androgen metabolism.