Identification of antidiabetic inhibitors from Allophylus villosus and Mycetia sinensis by targeting α-glucosidase and PPAR-γ: In-vitro, in-vivo, and computational evidence.

Diabetes mellitus (DM) is a metabolic disorder arising from insulin deficiency and defectiveness of the insulin receptor functioning on transcription factor where the body loses control to regulate glucose metabolism in ß-cells, pancreatic and liver tissues to homeostat glucose level. Mainstream medicines used for DM are incapable of restoring normal glucose homeostasis and have side effects where medicinal plant-derived medicine administrations have been claimed to cure diabetes or at least alleviate the significant symptoms and progression of the disease by the traditional practitioners. This study focused on screening phytocompounds and their pharmacological effects on anti-hyperglycemia on Swiss Albino mice of n-hexane, ethyl acetate, and ethanol extract of both plants Mycetia sinensis and Allophylus villosus as well as the in-silico investigations. Qualitative screening of phytochemicals and total phenolic and flavonoid content estimation were performed significantly in vitro analysis. FTIR and GC-MS analysis précised the functional groups and phytochemical investigations where FTIR scanned 14, 23 & 17 peaks in n-hexane, ethyl acetate, and ethanol extracts of Mycetia sinensis whereas the n-hexane, ethyl acetate, and ethanol extracts of Allophylus villosus scanned 11 peaks, 18 peaks, and 29 peaks, respectively. In GC-MS, 24 chemicals were identified in Mycetia sinensis extracts, whereas 19 were identified in Allophylus villosus extracts. Moreover, both plants' ethyl acetate and ethanol fractioned extracts were reported significantly (p < 0.05) with concentrations of 250 mg and 500 mg on mice for oral glucose tolerance test, serum creatinine test and serum alkaline phosphatase test. In In silico study, a molecular docking study was done on these 43 phytocompounds identified from Mycetia sinensis and Allophylus villosus to identify their binding affinity to the target Alpha Glucosidase (AG) and Peroxisome proliferator-activated receptor gamma protein (PPARG). Therefore, ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, quantum mechanics-based DFT (density-functional theory), and molecular dynamics simulation were done to assess the effectiveness of the selected phytocompounds. According to the results, phytocompounds such as 2,4-Dit-butyl phenyl 5-hydroxypentanoate and Diazo acetic acid (1S,2S,5R)-2-isopropyl-5-methylcyclohexyl obtained from Mycetia sinensis and Allophylus villosus extract possess excellent antidiabetic activities.

Big Data and Artificial Intelligence Modeling for Drug Discovery.

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Targeting the inward rectifier potassium channel 5.1 in thyroid cancer: artificial intelligence-facilitated molecular docking for drug discovery.

BACKGROUND: Recurrent and metastatic thyroid cancer is more invasive and can transform to dedifferentiated thyroid cancer, thus leading to a severe decline in the 10-year survival. The thyroid-stimulating hormone receptor (TSHR) plays an important role in differentiation process. We aim to find a therapeutic target in redifferentiation strategies for thyroid cancer. METHODS: Our study integrated the differentially expressed genes acquired from the Gene Expression Omnibus database by comparing TSHR expression levels in the Cancer Genome Atlas database. We conducted functional enrichment analysis and verified the expression of these genes by RT-PCR in 68 pairs of thyroid tumor and paratumor tissues. Artificial intelligence-enabled virtual screening was combined with the VirtualFlow platform for deep docking. RESULTS: We identified five genes (KCNJ16, SLC26A4, TG, TPO, and SYT1) as potential cancer treatment targets. TSHR and KCNJ16 were downregulated in the thyroid tumor tissues, compared with paired normal tissues. In addition, KCNJ16 was lower in the vascular/capsular invasion group. Enrichment analyses revealed that KCNJ16 may play a significant role in cell growth and differentiation. The inward rectifier potassium channel 5.1 (Kir5.1, encoded by KCNJ16) emerged as an interesting target in thyroid cancer. Artificial intelligence-facilitated molecular docking identified Z2087256678_2, Z2211139111_1, Z2211139111_2, and PV-000592319198_1 (-7.3 kcal/mol) as the most potent commercially available molecular targeting Kir5.1. CONCLUSION: This study may provide greater insights into the differentiation features associated with TSHR expression in thyroid cancer, and Kir5.1 may be a potential therapeutic target in the redifferentiation strategies for recurrent and metastatic thyroid cancer.

DrugRep: an automatic virtual screening server for drug repurposing.

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .

Creation of targeted compound libraries based on 3D shape recognition.

In the emerging field of drug discovery, rapid virtual screening methods become extremely valuable, especially when dealing with ultra-large databases of organic small bioactive molecules. In this work, we present a fast, computationally resource-efficient, and simple workflow for screening targeted compound libraries generated from ultra-large virtual chemical space. This workflow aims to find compounds with similar molecular 3D shapes with reference ones, and at the same time to expand chemical diversity and to identify new and potentially active scaffolds. This pipeline ensures the enrichment of the generated libraries with novel chemotypes. Also, it was shown that delicate tailoring of the physicochemical parameters of the search set ensures that all library compounds will possess desired property distributions. A visual inspection has shown that found structures bind to the receptor in the same way as the reference ones. Using our screening workflow, we have created a number of conventional protein-targeted libraries: the GPCRs Targeted Library (531 K compounds) and the Protein Kinases Targeted Library (113 K compounds). The described pipeline and scripts are freely accessible at: https://github.com/ChemSpace-LLC/usrcat_sim .

The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery.

Complex disorders, such as depression, remain a mystery for scientists. Although genetic factors are considered important for the prediction of one's vulnerability, it is hard to estimate the exact risk for a patient to develop depression, based only on one category of vulnerability criteria. Genetic factors also regulate drug metabolism, and when they are identified in a specific combination, may result in increased drug resistance. A proper understanding of the genetic basis of depression assists in the development of novel promising medications and effective disorder management schemes. This review aims to analyze the recent literature focusing on the correlation between specific genes and the occurrence of depression. Moreover, certain aspects targeting a high drug resistance identified among patients suffering from major depressive disorder were highlighted in this manuscript. An expected direction of future drug discovery campaigns was also discussed.

Structure-Based Discovery of Receptor Activator of Nuclear Factor-κB Ligand (RANKL)-Induced Osteoclastogenesis Inhibitors.

Receptor activator of nuclear factor-κB ligand (RANKL) has been actively pursued as a therapeutic target for osteoporosis, given that RANKL is the master mediator of bone resorption as it promotes osteoclast differentiation, activity and survival. We employed a structure-based virtual screening approach comprising two stages of experimental evaluation and identified 11 commercially available compounds that displayed dose-dependent inhibition of osteoclastogenesis. Their inhibitory effects were quantified through TRAP activity at the low micromolar range (IC50 < 5 µΜ), but more importantly, 3 compounds displayed very low toxicity (LC50 > 100 µΜ). We also assessed the potential of an N-(1-aryl-1H-indol-5-yl)aryl-sulfonamide scaffold that was based on the structure of a hit compound, through synthesis of 30 derivatives. Their evaluation revealed 4 additional hits that inhibited osteoclastogenesis at low micromolar concentrations; however, cellular toxicity concerns preclude their further development. Taken together with the structure-activity relationships provided by the hit compounds, our study revealed potent inhibitors of RANKL-induced osteoclastogenesis of high therapeutic index, which bear diverse scaffolds that can be employed in hit-to-lead optimization for the development of therapeutics against osteolytic diseases.

Identification of Sesamin from Sesamum indicum as a Potent Antifungal Agent Using an Integrated in Silico and Biological Screening Platform.

Due to the limited availability of antifungal drugs, their relevant side effects and considering the insurgence of drug-resistant strains, novel antifungal agents are urgently needed. To identify such agents, we have developed an integrated computational and biological screening platform. We have considered a promising drug target in antifungal drug discovery (exo-1,3-ß-glucanase) and a phytochemical library composed of bioactive natural products was used. These products were computationally screened against the selected target using molecular docking and molecular dynamics techniques along with the evaluation of drug-like profile. We selected sesamin as the most promising phytochemical endowed with a potential antifungal profile and satisfactory drug-like properties. Sesamin was submitted to a preliminary biological evaluation to test its capability to inhibit the growth of several Candida species by calculating the MIC/MFC and conducting synergistic experiments with the marketed drug fluconazole. Following the screening protocol, we identified sesamin as a potential exo-1,3-ß-glucanase inhibitor, with relevant potency in inhibiting the growth of Candida species in a dose-dependent manner (MIC and MFC of 16 and 32 µg/mL, respectively). Furthermore, the combination of sesamin with fluconazole highlighted relevant synergistic effects. The described screening protocol revealed the natural product sesamin as a potential novel antifungal agent, showing an interesting predicted pharmacological profile, paving the way to the development of innovative therapeutics against fungal infections. Notably, our screening protocol can be helpful in antifungal drug discovery.

Reckoning apigenin and kaempferol as a potential multi-targeted inhibitor of EGFR/HER2-MEK pathway of metastatic colorectal cancer identified using rigorous computational workflow.

In the past two decades, the treatment of metastatic colorectal cancer (mCRC) has been revolutionized as multiple cytotoxic, biological, and targeted drugs are being approved. Unfortunately, tumors treated with single targeted agents or therapeutics usually develop resistance. According to pathway-oriented screens, mCRC cells evade EGFR inhibition by HER2 amplification and/or activating Kras-MEK downstream signaling. Therefore, treating mCRC patients with dual EGFR/HER2 inhibitors, MEK inhibitors, or the combination of the two drugs envisaged to prevent the resistance development which eventually improves the overall survival rate. In the present study, we aimed to screen potential phytochemical lead compounds that could multi-target EGFR, HER2, and MEK1 (Mitogen-activated protein kinase kinase) using a computer-aided drug design approach that includes molecular docking, endpoint binding free energy calculation using MM-GBSA, ADMET, and molecular dynamics (MD) simulations. Docking studies revealed that, unlike all other ligands, apigenin and kaempferol exhibit the highest docking score against all three targets. Details of ADMET analysis, MM/GBSA, and MD simulations helped us to conclusively determine apigenin and kaempferol as potentially an inhibitor of EGFR, HER2, and MEK1 apigenin and kaempferol against mCRC at a systemic level. Additionally, both apigenin and kaempferol elicited antiangiogenic properties in a dose-dependent manner. Collectively, these findings provide the rationale for drug development aimed at preventing CRC rather than intercepting resistance.

In Silico Identification of Potential Insect Peptides against Biofilm-Producing Staphylococcus aureus.

Biofilm-producing Staphylococcus aureus (SA) strains are frequently found in medical environments, from surgical/ wound sites, medical devices. These biofilms reduce the efficacy of applied antibiotics during the treatment of several infections, such as cystic fibrosis, endocarditis, or urinary tract infections. Thus, the development of potential therapeutic agents to destroy the extra protective biofilm layers or to inhibit the biofilm-producing enzymes is urgently needed. Advanced and cost-effective bioinformatics tools are advantageous in locating and speeding up the selection of antibiofilm candidates. Based on the potential drug characteristics, we have selected one-hundred thirty-three antibacterial peptides derived from insects to assess for their antibiofilm potency via molecular docking against five putative biofilm formation and regulated target enzymes: the staphylococcal accessory regulator A or SarA (PDB ID: 2FRH), 4,4'-diapophytoene synthase or CrtM (PDB ID: 2ZCQ), clumping factor A or ClfA (PDB ID: 1N67) and serine-aspartate repeat protein C or SdrC (PDB ID: 6LXH) and sortase A or SrtA (PDB ID: 1T2W) of SA bacterium. In this study, molecular docking was performed using HPEPDOCK and HDOCK servers, and molecular interactions were examined using BIOVIA Discovery Studio Visualizer-2019. The docking score (kcal/mol) range of five promising antibiofilm peptides against five targets was recorded as follows: diptericin A (-215.52 to -303.31), defensin (-201.11 to -301.92), imcroporin (-212.08 to -287.64), mucroporin (-228.72 to -286.76), apidaecin II (-203.90 to -280.20). Among these five, imcroporin and mucroporin were 13 % each, while defensin contained only 1 % of positive net charged residues (Arg+Lys) projected through ProtParam and NetWheels tools. Similarly, imcroporin, mucroporin and apidaecin II were 50 %, while defensin carried 21.05 % of hydrophobic residues predicted by the tool PEPTIDE. 2.0. Most of the peptides exhibited potential characteristics to inhibit S. aureus-biofilm formation via disrupting the cell membrane and cytoplasmic integrity. In summary, the proposed hypothesis can be considered a cost-effective platform for selecting the most promising bioactive drug candidates within a limited timeframe with a greater chance of success in experimental and clinical studies.

Resveratrol-like Compounds as SIRT1 Activators.

The sirtuin 1 (SIRT1) activator resveratrol has emerged as a promising candidate for the prevention of vascular oxidative stress, which is a trigger for endothelial dysfunction. However, its clinical use is limited by low oral bioavailability. In this work, we have applied a previously developed computational protocol to identify the most promising derivatives from our in-house chemical library of resveratrol derivatives. The most promising compounds in terms of SIRT1 activation and oral bioavailability, predicted in silico, were evaluated for their ability to activate the isolated SIRT1 enzyme. Then, we assessed the antioxidant effects of the most effective derivative, compound 3d, in human umbilical vein endothelial cells (HUVECs) injured with H2O2 100 µM. The SIRT1 activator 3d significantly preserved cell viability and prevented an intracellular reactive oxygen species increase in HUVECs exposed to the oxidative stimulus. Such effects were partially reduced in the presence of a sirtuin inhibitor, sirtinol, confirming the potential role of sirtuins in the activity of resveratrol and its derivatives. Although 3d appeared less effective than resveratrol in activating the isolated enzyme, the effects exhibited by both compounds in HUVECs were almost superimposable, suggesting a higher ability of 3d to cross cell membranes and activate the intracellular target SIRT1.

Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates.

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.

Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery.

We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users' local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps-which run calculations "in the cloud" rather than on users' local computers-browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.

Crystal Structure of Human CD47 in Complex with Engineered SIRPα.D1(N80A).

Background: Targeting the CD47/SIRPα signaling pathway represents a novel approach to enhance anti-tumor immunity. However, the crystal structure of the CD47/SIRPα has not been fully studied. This study aims to analyze the structure interface of the complex of CD47 and IMM01, a novel recombinant SIRPα-Fc fusion protein. Methods: IMM01-Fab/CD47 complex was crystalized, and diffraction images were collected. The complex structure was determined by molecular replacement using the program PHASER with the CD47-SIRPαv2 structure (PDB code 2JJT) as a search model. The model was manually built using the COOT program and refined using TLS parameters in REFMAC from the CCP4 program suite. Results: Crystallization and structure determination analysis of the interface of IMM01/CD47 structure demonstrated CD47 surface buried by IMM01. Comparison with the literature structure (PDB ID 2JJT) showed that the interactions of IMM01/CD47 structure are the same. All the hydrogen bonds that appear in the literature structure are also present in the IMM01/CD47 structure. These common hydrogen bonds are stable under different crystal packing styles, suggesting that these hydrogen bonds are important for protein binding. In the structure of human CD47 in complex with human SIRPα, except SER66, the amino acids that form hydrogen bonds are all conserved. Furthermore, comparing with the structure of PDB ID 2JJT, the salt bridge interaction from IMM01/CD47 structure are very similar, except the salt bridge bond between LYS53 in IMM01 and GLU106 in CD47, which only occurs between the B and D chains. However, as the side chain conformation of LYS53 in chain A is slightly different, the salt bridge bond is absent between the A and C chains. At this site between chain A and chain C, there are a salt bridge bond between LYS53 (A) and GLU104 (C) and a salt bridge bond between HIS56 (A) and GLU106 (C) instead. According to the sequence alignment results of SIRPα, SIRPß and SIRPγ in the literature of PDB ID 2JJT, except ASP100, the amino acids that form common salt bridge bonds are all conserved. Conclusion: Our data demonstrated crystal structure of the IMM01/CD47 complex and provides a structural basis for the structural binding interface and future clinical applications.

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

Development of Novel Inhibitors Targeting the D-Box of the DNA Binding Domain of Androgen Receptor.

The inhibition of the androgen receptor (AR) is an established strategy in prostate cancer (PCa) treatment until drug resistance develops either through mutations in the ligand-binding domain (LBD) portion of the receptor or its deletion. We previously identified a druggable pocket on the DNA binding domain (DBD) dimerization surface of the AR and reported several potent inhibitors that effectively disrupted DBD-DBD interactions and consequently demonstrated certain antineoplastic activity. Here we describe further development of small molecule inhibitors of AR DBD dimerization and provide their broad biological characterization. The developed compounds demonstrate improved activity in the mammalian two-hybrid assay, enhanced inhibition of AR-V7 transcriptional activity, and improved microsomal stability. These findings position us for the development of AR inhibitors with entirely novel mechanisms of action that would bypass most forms of PCa treatment resistance, including the truncation of the LBD of the AR.

A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19.

In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300-400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization.

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

Artificial Intelligence for Autonomous Molecular Design: A Perspective.

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.