Drug Design Approach Research Articles

Abstract 1183: A first-in-class and SHP1 allosteric inhibitor, SB8091, shows a good efficacy and safety profile in preclinical models

Abstract Introduction: Src homology 2 domain-containing phosphatase 1 (SHP1) is a cytoplasmic protein tyrosine phosphatase (PTP) that is primarily expressed in hematopoietic cells and is commonly recognized as a negative regulator of multiple signaling pathways1. Previously, we showed that inhibition of the phosphatase activity of SHP1 by a novel SHP1 allosteric inhibitor, SB6299, results in the activation of innate and adaptive immune cells and enhanced anti-tumor immune responses2. Here, we report SB8091, an advanced SHP1 allosteric inhibitor with a promising initial safety profile and improved pharmacokinetic properties. Methods: SHP1 inhibitors were developed through a structure-based drug design approach, and optimized for cellular activity on target, in vitro ADME, and in vivo pharmacokinetic properties. Pharmacokinetic (PK) and preliminary toxicity studies were performed with mice. The lead series was assessed in in-vitro studies with mouse or human primary immune cells. In-vivo efficacy and pharmacodynamic data were generated using mouse syngeneic tumor models. Results: Structurally, SB8091 seemed to stabilize the autoinhibitory form of SHP1 as an allosteric inhibitor, in a nanomolar range and which can explain the outstanding selectivity compared to other PTP enzymes, including SHP2. In several human primary immune cell types, SB8091 significantly increased cytokine responses and demonstrated significantly enhanced activity in NK cells and macrophages against various cancer target cells. As a single agent, this compound exhibited inhibitory effects on tumor growth when administered orally, and the efficacy was further enhanced when co-administered with anti-PD1 antibodies in various syngeneic tumor models. Notably, this effect was associated with a pharmacodynamic response marked by increased levels of phosphorylated substrates regulated by SHP1. SB8091 exhibited favorable pharmacokinetic properties for oral administration, and SafetyScreen data showed a very low potential for off-target activity and drug-drug interactions. Additionally, during a 2-week repeat pilot toxicity study in mice, SB8091 demonstrated excellent tolerability even at high doses, indicating a high therapeutic index (TI > 100). Conclusion: Our advanced compound, SB8091, is a potent, highly selective, and orally bioavailable allosteric inhibitor of SHP1. It has demonstrated excellent efficacy as a monotherapy or in combination with immune checkpoint blockers, with favorable drug properties and safety profiles. Research to advance to the clinical stage is being conducted.

Abstract 4507: Discovery of a smart molecular glue: A small-molecular compound selectively degrades Nrf2 in KEAP1-mutated tumor cells, by restoring the broken KEAP1 mutant/Nrf2 interaction

Abstract Overcoming therapeutic resistance without incurring prohibitive normal tissue toxicity is a great challenge in anticancer therapies. Transcription factor NRF2 is a master regulator of cellular protective response, which provides protective effects in normal tissues. KEAP1, the endogenous NRF2 inhibitor, binds NRF2 and redirect it towards proteasome-dependent degradation. KEAP1/NRF2 interaction is therefore critical for maintaining NRF2 at a basal level. Accumulated studies revealed cancer cells could hijack NRF2 pathway to confer drug resistance. A number of clinically-relevant KEAP1 mutations were shown to disrupt the KEAP1/NRF2 interaction, leading to elevated NRF2 level and confer drug resistance. Here, by structure-based drug design approach, we discovered a small-molecule NRF2 inhibitor, R16, which selectively binds KEAP1 mutants and restores their NRF2-inhibitory function in tumor cells. We performed in silico screening against the NCI-open database. Top-ranking candidates were initially evaluated by ARE-luc reporter assays, leading to discovery of R16. Effect of R16 in restoring interaction between KEAP1 mutants and NRF2 were evaluated by BRET2 assays. Among 14 KEAP1 mutations that result in disruption of KEAP1/NRF2 interaction, R16 is active against 11 of them. A variety of assays, including tryptophan quenching, cellular target engagement, BRET2 and GST-pull down assays, indicated that R16 selectively engage KEAP1 mutants and restore their interaction with NRF2, leading to NRF2 degradation. R16 at 0.5 uM substantially sensitizes KEAP1-mutated tumor cells to cisplatin and gefitinib, with no obvious effect in wild-type KEAP1 cells. Importantly, R16 showed significant in-vivo efficacy in sensitizing A549 xenograft bearing KEAP1 G333C mutation to cisplatin, while it has no effect on xenograft with wild-type KEAP1. KEAP1-mutated tumors are emerging as a sub-group with severe resistance to currently available therapies. Here, we have identified a KEAP1 mutant-selective NRF2 inhibitor, which potently sensitizes KEAP1-mutated cells/tumors to anticancer agents, with little effects on the WT-KEAP1. Our work demonstrated it is feasible to restore the NRF2-inhibitory function of KEAP1 mutants, by targeting the mutants with compounds to repair the disrupted KEAP1 mutant/NRF2 interactions. The KEAP1 mutant-selectivity is critical, as NRF2 is a master regulator of cellular defense response in WT-KEAP1 normal tissues. Citation Format: Xiaohong Tian, Tahar Aboulkassim, Qiang Liu, Mark Hancock, Jian Hui Wu, Gerald Batist. Discovery of a smart molecular glue: A small-molecular compound selectively degrades Nrf2 in KEAP1-mutated tumor cells, by restoring the broken KEAP1 mutant/Nrf2 interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4507.

Biological New Targets Prediction & ADME Pharmacokinetics Profiling of Newly Synthesized E / Z Isomers of Methyl 2-phenyl-2-(2-phenylhydrazono) Acetate Derivatives

Objective: In order to predict the pharmacokinetic properties, and new potential biological targets, biological targets prediction, and absorption, distribution, metabolism and excretion (ADME) profiling of newly synthesized Entgegen / Zusammen (E / Z isomers) of Methyl 2-phenyl-2-(2-phenylhydrazono) acetate derivatives were carried out. Methods: The prediction of new biological targets, pharmacokinetic properties, membrane permeability, and bioavailability radar properties were carried out by using Swiss Targets Prediction and Swiss ADME tools, respectively. Results: All the eight screened compounds possessed excellent drug-likeness criteria and passed successfully from Pan-assay interference compounds filter. Additionally, all screened compounds fell within the acceptable range in the bioavailability radar, showing excellent descriptors with a slight exceeding of insaturation properties. Compounds displayed elevated permeability across the Blood Brain Barrier, while compounds exhibited highest Human Intestinal Absorption according to the Egan Egg model. Moreover, the screened compounds also exhibited good pharmacokinetic properties. All the compounds exhibited activity against various enzymes, such as lyase, kinase, protease, and Family AG protein-coupled receptor. Conclusion: This conducted study smartly reveals that in-silico based studies were considered to provide robustness towards a rational drug design and development approach; therefore, in this way, it helps for further investigation of drug design and drug discovery steps.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach.

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

1,3,5-triazine derivatives as potential anticancer agents against lung and breast cancer cell lines: Synthesis, biological evaluation, and structure-based drug design studies

Morpholine substituted 1,3,5-triazines have been recently recognized as potent inhibitors of PI3K-mTOR kinase signalling pathway. In this paper, we have performed synthesis and in vitro anti-cancer activity of trisubstituted 1,3,5-triazine derivatives against the A549 (Lung cancer) and MCF-7 (Breast cancer) cell lines. All synthesized analogs showed good inhibitory activity against both cell lines. Out of ten compounds, only two compounds, benzyl-4-(4-(4-(4-fluorophenyl)piperazin-1-yl)-6-morpholino-1,3,5-triazin-2-yl)piperazine-1-carboxylate (Compound 6b), shows 9.61 µM anticancer activity against A549 cell line and 22.68 µM activity against MCF-7 cell lines. Similarly, benzyl-4-(4-morpholino-6-thiomorpholino-1,3,5-triazin-2-yl)piperazine-1-carboxylate (Compound 6c) shows 39.70 µM anticancer activity against A549 cell line and 12.88 µM activity against MCF-7 cell lines. It shows that compound 6b is a potent inhibitor for A549 lung cancer cell line, and compound 6c shows excellent inhibitory activity for MCF-7 breast cancer cell line (9.16 µM and 12.88 µM, respectively). Both compounds (Compound 6b and Compound 6c) did not show toxicity to the non-cancerous HaCaT (Immortalized human keratinocytes) cells. Additionally, molecular descriptors of all synthesized compounds (6a-j) were calculated. After in vitro studies, the potent compounds 6b and 6c were validated using molecular docking and dynamics simulation studies and informed their binding affinities and conformational stability in the binding cavity. Structure-based drug design approach was followed for designing compounds based on potential ligand-receptor interaction. The result shows that triazine analogs serve as lead structures for generating new anti-cancer drugs.

Fragment-based drug design of novel inhibitors targeting lipoprotein (a) kringle domain KIV-10-mediated cardiovascular disease.

Cardiovascular diseases (CVDs) are the leading cause of death globally, attributed to a complex etiology involving metabolic, genetic, and protein-related factors. Lipoprotein(a) (Lp(a)), identified as a genetic risk factor, exhibits elevated levels linked to an increased risk of cardiovascular diseases. The lipoprotein(a) kringle domains have recently been identified as a potential target for the treatment of CVDs, in this study we utilized a fragment-based drug design approach to design a novel, potent, and safe inhibitor for lipoprotein(a) kringle domain. With the use of fragment library (61,600 fragments) screening, combined with analyses such as MM/GBSA, molecular dynamics simulation (MD), and principal component analysis, we successfully identified molecules effective against the kringle domains of Lipoprotein(a). The hybridization process (Breed) of the best fragments generated a novel 249 hybrid molecules, among them 77 exhibiting superior binding affinity (≤ -7kcal/mol) compared to control AZ-02 (-6.9kcal/mol), Importantly, the top ten molecules displayed high similarity to the control AZ-02. Among the top ten molecules, BR1 exhibited the best docking energy (-11.85kcal/mol ), and higher stability within the protein LBS site, demonstrating the capability to counteract the pathophysiological effects of lipoprotein(a) [Lp(a)]. Additionally, principal component analysis (PCA) highlighted a similar trend of motion during the binding of BR1 and the control compound (AZ-02), limiting protein mobility and reducing conformational space. Moreover, ADMET analysis indicated favorable drug-like properties, with BR1 showing minimal violations of Lipinski's rules. Overall, the identified compounds hold promise as potential therapeutics, addressing a critical need in cardiovascular medicine. Further preclinical and clinical evaluations are needed to validate their efficacy and safety, potentially ushering in a new era of targeted therapies for CVDs.

Discovery and Optimization of Selective Brain-Penetrant EBP Inhibitors that Enhance Oligodendrocyte Formation.

The inhibition of emopamil binding protein (EBP), a sterol isomerase within the cholesterol biosynthesis pathway, promotes oligodendrocyte formation, which has been proposed as a potential therapeutic approach for treating multiple sclerosis. Herein, we describe the discovery and optimization of brain-penetrant, orally bioavailable inhibitors of EBP. A structure-based drug design approach from literature compound 1 led to the discovery of a hydantoin-based scaffold, which provided balanced physicochemical properties and potency and an improved in vitro safety profile. The long half-lives of early hydantoin-based EBP inhibitors in rodents prompted an unconventional optimization strategy, focused on increasing metabolic turnover while maintaining potency and a brain-penetrant profile. The resulting EBP inhibitor 11 demonstrated strong in vivo target engagement in the brain, as illustrated by the accumulation of EBP substrate zymostenol after repeated dosing. Furthermore, compound 11 enhanced the formation of oligodendrocytes in human cortical organoids, providing additional support for our therapeutic hypothesis.

Quinazolin-4-one Derivatives: Enoyl-acyl Carrier Protein (ACP) Reductase (InhA) Antagonists Using in silico Drug Design Approach

Quinazolin-4-one Derivatives: Enoyl-acyl Carrier Protein (ACP) Reductase (InhA) Antagonists Using in silico Drug Design Approach

The thermodynamics of neurodegenerative disease

The formation of protein aggregates in the brain is a central aspect of the pathology of many neurodegenerative diseases. This self-assembly of specific proteins into filamentous aggregates, or fibrils, is a fundamental biophysical process that can easily be reproduced in the test tube. However, it has been difficult to obtain a clear picture of how the biophysical insights thus obtained can be applied to the complex, multi-factorial diseases and what this means for therapeutic strategies. While new, disease-modifying therapies are now emerging, for the most devastating disorders, such as Alzheimer's and Parkinson's disease, they still fall well short of offering a cure, and few drug design approaches fully exploit the wealth of mechanistic insights that has been obtained in biophysical studies. Here, I attempt to provide a new perspective on the role of protein aggregation in disease, by phrasing the problem in terms of a system that, under constant energy consumption, attempts to maintain a healthy, aggregate-free state against the thermodynamic driving forces that inexorably push it toward pathological aggregation.

Development of a Small Molecule Downmodulator for the Transcription Factor Brachyury

AbstractBrachyury is an oncogenic transcription factor whose overexpression drives chordoma growth. The downmodulation of brachyury in chordoma cells has demonstrated therapeutic potential, however, as a transcription factor it is classically deemed “undruggable”. Given that direct pharmacological intervention against brachyury has proven difficult, attempts at intervention have instead targeted upstream kinases. Recently, afatinib, an FDA‐approved kinase inhibitor, has been shown to modulate brachyury levels in multiple chordoma cell lines. Herein, we use afatinib as a lead to undertake a structure‐based drug design approach, aided by mass‐spectrometry and X‐ray crystallography, to develop DHC‐156, a small molecule that more selectively binds brachyury and downmodulates it as potently as afatinib. We eliminated kinase‐inhibition from this novel scaffold while demonstrating that DHC‐156 induces the post‐translational downmodulation of brachyury that results in an irreversible impairment of chordoma tumor cell growth. In doing so, we demonstrate the feasibility of direct brachyury modulation, which may further be developed into more potent tool compounds and therapies.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Development of a Small Molecule Downmodulator for the Transcription Factor Brachyury.

Brachyury is an oncogenic transcription factor whose overexpression drives chordoma growth. The downmodulation of brachyury in chordoma cells has demonstrated therapeutic potential, however, as a transcription factor it is classically deemed "undruggable". Given that direct pharmacological intervention against brachyury has proven difficult, attempts at intervention have instead targeted upstream kinases. Recently, afatinib, an FDA-approved kinase inhibitor, has been shown to modulate brachyury levels in multiple chordoma cell lines. Herein, we use afatinib as a lead to undertake a structure-based drug design approach, aided by mass-spectrometry and X-ray crystallography, to develop DHC-156, a small molecule that more selectively binds brachyury and downmodulates it as potently as afatinib. We eliminated kinase-inhibition from this novel scaffold while demonstrating that DHC-156 induces the post-translational downmodulation of brachyury that results in an irreversible impairment of chordoma tumor cell growth. In doing so, we demonstrate the feasibility of direct brachyury modulation, which may further be developed into more potent tool compounds and therapies.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from −8.0 to −9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07–0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25–0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

Exploring Lead-Like Molecules of Traditional Chinese Medicine for Treatment Quest against Aliarcobacter butzleri: In Silico Toxicity Assessment, Dynamics Simulation, and Pharmacokinetic Profiling.

Aliarcobacter butzleri is a Gram-negative, curved or spiral-shaped, microaerophilic bacterium and causes human infections, specifically diarrhea, fever, and sepsis. The research objective of this study was to employ computer-aided drug design techniques to identify potential natural product inhibitors of a vital enzyme in this bacterium. The pyrimidine biosynthesis pathway in its core genome fraction is crucial for its survival and presents a potential target for novel therapeutics. Hence, novel small molecule inhibitors were identified (from traditional Chinese medicinal (TCM) compound library) against it, which may be used for possible curbing of infection by A. butzleri. Methods. A comprehensive subtractive genomics approach was utilized to identify a key enzyme (orotidine-5'-phosphate decarboxylase) cluster conserved in the core genome fraction of A. butzleri. It was selected for inhibitor screening due to its vital role in pyrimidine biosynthesis. TCM library (n > 36,000 compounds) was screened against it using pharmacophore model based on orotidylic acid (control), and the obtained lead-like molecules were subjected to structural docking using AutoDock Vina. The top-scoring compounds, ZINC70454134, ZINC85632684, and ZINC85632721, underwent further scrutiny via a combination of physiological-based pharmacokinetics, toxicity assessment, and atomic-scale dynamics simulations (100 ns). Among the screened compounds, ZINC70454134 displayed the most favorable characteristics in terms of binding, stability, absorption, and safety parameters. Overall, traditional Chinese medicine (TCM) compounds exhibited high bioavailability, but in diseased states (cirrhosis, renal impairment, and steatosis), there was a significant decrease in absorption, Cmax, and AUC of the compounds compared to the healthy state. Furthermore, MD simulation demonstrated that the ODCase-ZINC70454134 complex had a superior overall binding affinity, supported by PCA proportion of variance and eigenvalue rank analysis. These favorable characteristics underscore its potential as a promising drug candidate. The computer-aided drug design approach employed for this study helped expedite the discovery of antibacterial compounds against A. butzleri, offering a cost-effective and efficient approach to address infection by it. It is recommended that ZINC70454134 should be considered for further experimental analysis due to its indication as a potential therapeutic agent for combating A. butzleri infections. This study provides valuable insights into the molecular basis of biophysical inhibition of A. butzleri through TCM compounds.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities.

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.

In silico Structure-based Screening of Potential Anticancer Bioactive Natural Constituents from African Natural Products

Introduction: Inhibitors of topoisomerases, essential regulators of cancer development, are promising as cancer treatments. These enzymes regulate DNA topology and eliminate topological constraints during various biological processes, including replication, transcription, and recombination. Nature has continually offered scientists pathways to explore the development of new drugs. Indeed, since ancient times, various plant extracts have been utilized in treating multiple pathologies. Objective: It’s intriguing to diversify the therapeutic classes of natural topoisomerase 1 inhibitors. We aimed to explore the relationship between the toxicity of certain medicinal plants in North Africa and their anti-topoisomerase 1 enzyme activity. This investigation aims to discover potentially valuable compounds for fighting cancer by inhibiting the Topo1 enzyme, enriching the anticancer therapeutic class. Methods: This study has conducted a virtual screening of the African Natural Products Database to identify new scaffolds as topoisomerase 1 inhibitors. Molecular docking as a structure-based drug design approach was selected as one of the best approaches, and the complex code ID: 1K4T was used for this purpose. Results and Discussion: The molecular docking of more than 5790 natural products extracted from this database was docked into the binding site of the above-cited complex using the Modlock optimizer and Moldock score as search and scoring function algorithms, respectively. The top-ranked compounds have been assessed, analyzed, and compared to Topotecan and Irinotecan as reference ligands and drugs. Conclusion: Consequently, the seven natural products have shown a strong affinity to topoisomerase 1 and DNA. They establish a clear link between topoisomerase 1 inhibition and the anticancer activity of their corresponding plant extracts. Therefore, these hits are promising and serve as a base for further development of new topoisomerase 1 inhibitors.

Targeting with Structural Analogs of Natural Products the Purine Salvage Pathway in Leishmania (Leishmania) infantum by Computer-Aided Drug-Design Approaches.

Visceral Leishmaniasis (VL) has a high death rate, with 500,000 new cases and 50,000 deaths occurring annually. Despite the development of novel strategies and technologies, there is no adequate treatment for the disease. Therefore, the purpose of this study is to find structural analogs of natural products as potential novel drugs to treat VL. We selected structural analogs from natural products that have shown antileishmanial activities, and that may impede the purine salvage pathway using computer-aided drug-design (CADD) approaches. For these, we started with the vastly studied target in the pathway, the adenine phosphoribosyl transferase (APRT) protein, which alone is non-essential for the survival of the parasite. Keeping this in mind, we search for a substance that can bind to multiple targets throughout the pathway. Computational techniques were used to study the purine salvage pathway from Leishmania infantum, and molecular dynamic simulations were used to gather information on the interactions between ligands and proteins. Because of its low homology to human proteins and its essential role in the purine salvage pathway proteins network interaction, the findings further highlight the significance of adenylosuccinate lyase protein (ADL) as a therapeutic target. An analog of the alkaloid Skimmianine, N,N-diethyl-4-methoxy-1-benzofuran-6-carboxamide, demonstrated a good binding affinity to APRT and ADL targets, no expected toxicity, and potential for oral route administration. This study indicates that the compound may have antileishmanial activity, which was granted in vitro and in vivo experiments to settle this finding in the future.

De Novo Drug Design Using Transformer-Based Machine Translation and Reinforcement Learning of an Adaptive Monte Carlo Tree Search.

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder-Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards their targets. We successfully integrated the Encoder-Decoder Transformer architecture, which generates molecular structures (drugs) from scratch with the RL-MCTS, serving as a reinforcement learning framework. The RL-MCTS combines the exploitation and exploration capabilities of a Monte Carlo Tree Search with the machine translation of a transformer-based Encoder-Decoder model. This dynamic approach allows the model to iteratively refine its drug candidate generation process, ensuring that the generated molecules adhere to essential physicochemical and biological constraints and effectively bind to their targets. The results from drugAI showcase the effectiveness of the proposed approach across various benchmark datasets, demonstrating a significant improvement in both the validity and drug-likeness of the generated compounds, compared to two existing benchmark methods. Moreover, drugAI ensures that the generated molecules exhibit strong binding affinities to their respective targets. In summary, this research highlights the real-world applications of drugAI in drug discovery pipelines, potentially accelerating the identification of promising drug candidates for a wide range of diseases.

Computational fragment-based drug design of potential Glo-I inhibitors

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0–18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.