Field Of Drug Discovery Research Articles

Nickel-Electrocatalytic Decarboxylative Arylation to Access Quaternary Centers.

There is a pressing need, particularly in the field of drug discovery, for general methods that will enable direct coupling of tertiary alkyl fragments to (hetero)aryl halides. Herein a uniquely powerful and simple set of conditions for achieving this transformation with unparalleled generality and chemoselectivity is disclosed. This new protocol is placed in context with other recently reported methods, applied to simplify the routes of known bioactive building blocks molecules, and scaled up in both batch and flow. The role of pyridine additive as well as the mechanism of this reaction are interrogated through Cyclic Voltammetry studies, titration experiments, control reactions with Ni(0) and Ni(II)-complexes, and ligand optimization data. Those studies indicate that the formation of a BINAPNi(0) is minimized and the formation of an active pyridine-stabilized Ni(I) species is sustained during the reaction. Our preliminary mechanistic studies ruled out the involvement of Ni(0) species in this electrochemical cross-coupling, which is mediated by Ni(I) species via a Ni(I)-Ni(II)-Ni(III)-Ni(I) catalytic cycle.

Nickel‐Electrocatalytic Decarboxylative Arylation to Access Quaternary Centers**

AbstractThere is a pressing need, particularly in the field of drug discovery, for general methods that will enable direct coupling of tertiary alkyl fragments to (hetero)aryl halides. Herein a uniquely powerful and simple set of conditions for achieving this transformation with unparalleled generality and chemoselectivity is disclosed. This new protocol is placed in context with other recently reported methods, applied to simplify the routes of known bioactive building blocks molecules, and scaled up in both batch and flow. The role of pyridine additive as well as the mechanism of this reaction are interrogated through Cyclic Voltammetry studies, titration experiments, control reactions with Ni(0) and Ni(II)‐complexes, and ligand optimization data. Those studies indicate that the formation of a BINAPNi(0) is minimized and the formation of an active pyridine‐stabilized Ni(I) species is sustained during the reaction. Our preliminary mechanistic studies ruled out the involvement of Ni(0) species in this electrochemical cross‐coupling, which is mediated by Ni(I) species via a Ni(I)‐Ni(II)‐Ni(III)‐Ni(I) catalytic cycle.

Mechanism of Ligand Binding to Theophylline RNA Aptamer.

Studying RNA-ligand interactions and quantifying their binding thermodynamics and kinetics are of particular relevance in the field of drug discovery. Here, we combined biochemical binding assays and accelerated molecular simulations to investigate ligand binding and dissociation in RNA using the theophylline-binding RNA as a model system. All-atom simulations using a Ligand Gaussian accelerated Molecular Dynamics method (LiGaMD) have captured repetitive binding and dissociation of theophylline and caffeine to RNA. Theophylline's binding free energy and kinetic rate constants align with our experimental data, while caffeine's binding affinity is over 10,000 times weaker, and its kinetics could not be determined. LiGaMD simulations allowed us to identify distinct low-energy conformations and multiple ligand binding pathways to RNA. Simulations revealed a "conformational selection" mechanism for ligand binding to the flexible RNA aptamer, which provides important mechanistic insights into ligand binding to the theophylline-binding model. Our findings suggest that compound docking using a structural ensemble of representative RNA conformations would be necessary for structure-based drug design of flexible RNA.

Anti-Cancer and Anti-Oxidant Bioactive Metabolites from Aspergillus fumigatus WA7S6 Isolated from Marine Sources: In Vitro and In Silico Studies.

Cancer is a huge global disease burden. Every year, tens of millions of people worldwide are diagnosed with cancer, and more than half of them die as a result of it. The great biodiversity of the marine environment has increasingly piqued the interest of experts, especially in the field of drug discovery. The marine fungus Aspergillus fumigatus WA7S6 has been selected among a group of fungi isolated from marine sponges as it exhibits a pronounced antimicrobial activity toward a group of pathogenic microbes. The fungus has been identified genetically by amplification and analysis of its 18srRNA gene. The fungus crude extract has been obtained by cultivation of the fungus on rice media. The crude extract was tested for antibacterial activity against a variety of pathogenic microorganisms. The results demonstrated a pronounced antimicrobial action against P. aeruginosa, S. aureus, A. niger, and Candida albicans. Furthermore, we tested the antioxidant potential of the Aspergillus fumigatus WA7S6 crude extract using three different methods: ATBS, DPPH, and lipid peroxidation assays. Results showed that the crude extract WA7S6 had an IC50 value of 21.35 µg/mL. The anticancer potential of the crude extract was also evaluated against cancer cell lines such as Hela, MCF, and WI-38. The chemical profiling of the fungus extract was identified via GC-mass and in silico molecular docking of the identified compounds on heme oxygenase, as a stress protein included in cellular protection, antioxidant, and anti-inflammatory activities, suggesting that some compounds, such as 9-Tetradecynoic acid, 11-Hexadecynoic acid, methyl ester, and dehydromevalonic lactone, could be relevant for antioxidant purposes.

Diversity-Orientated Synthesis and Biological Properties of Compounds Based on the N-Phenylquinoneimine Scaffold.

The N-phenylquinoneimine scaffold is a versatile synthetic platform that has gained significant attention in the field of drug discovery due to its structural diversity and capacity to interact with biologically relevant targets. This review explores established synthetic methodologies and highlights the significant biological activities exhibited by compounds derived from this scaffold, their implications for medicinal chemistry, and the development of novel therapeutics.

De Novo Drug Design by Multi-Objective Path Consistency Learning with Beam A∗ Search.

Generating high-quality and drug-like molecules from scratch within the expansive chemical space presents a significant challenge in the field of drug discovery. In prior research, value-based reinforcement learning algorithms have been employed to generate molecules with multiple desired properties iteratively. The immediate reward was defined as the evaluation of intermediate-state molecules at each step, and the learning objective would be maximizing the expected cumulative evaluation scores for all molecules along the generative path. However, this definition of the reward was misleading, as in reality, the optimization target should be the evaluation score of only the final generated molecule. Furthermore, in previous works, randomness was introduced into the decision-making process, enabling the generation of diverse molecules but no longer pursuing the maximum future rewards. In this paper, immediate reward is defined as the improvement achieved through the modification of the molecule to maximize the evaluation score of the final generated molecule exclusively. Originating from the A ∗ search, path consistency (PC), i.e., f values on one optimal path should be identical, is employed as the objective function in the update of the f value estimator to train a multi-objective de novo drug designer. By incorporating the f value into the decision-making process of beam search, the DrugBA∗ algorithm is proposed to enable the large-scale generation of molecules that exhibit both high quality and diversity. Experimental results demonstrate a substantial enhancement over the state-of-theart algorithm QADD in multiple molecular properties of the generated molecules.

SadNet: a novel multimodal fusion network for protein-ligand binding affinity prediction.

Protein-ligand binding affinity prediction plays an important role in the field of drug discovery. Existing deep learning-based approaches have significantly improved the efficiency of protein-ligand binding affinity prediction through their excellent inductive bias capability. However, these methods only focus on fragmented three-dimensional data, which truncates the integrity of pocket data, leading to the neglect of potential long-range interactions. In this paper, we propose a dual-stream framework, with amino acid sequence assisting the atomic data fusion for graph neural network (termed SadNet), to fuse both 3D atomic data and sequence data for more accurate prediction results. In detail, SadNet consists of a pocket module and a sequence module. The sequence module expands the "receptive field" of the pocket module through a mid-term virtual node fusion. To better integrate sequence-level information from the sequence module and 3D structural information from the pocket module, we incorporate structural information for each amino acid within the sequence module. Besides, to better understand the intrinsic relationship between sequences and 3D atomic information, our SadNet utilizes information stacking from both the early stage and later stage. Experimental results on publicly available benchmark datasets demonstrate the superiority of the proposed dual-stream approach over the state-of-the-art alternatives. The code of this work is available online at https://github.com/wardhong/SadNet.

Synthesis of an 8-membered oxygen-containing benzo-fused heterocycle using flow technologies – an exercise in undertaking research with sustainability as a driver

Due to their natural abundance and biological properties, benzo-fused heterocycles are attractive targets in the field of drug discovery.

Synthesis and biological activity of rhodanine-furan conjugates: A review

Rhodanines are recognized as privileged heterocycles in medicinal chemistry. The main achievements include the development of drug-like molecules with numerous biological activities as well as approved drugs. The Furan nucleus is considered one of the promising heterocyclic cores in medicinal chemistry that showed numerous ranges of activity. The combination of several heterocycles in a one molecule commonly provides much more interest in the enhanced activity profile of its analogs than their parent separate constituents. Such conjugates are promising objects for modern medicinal chemistry. In this review paper recent advances in the synthesis and biological activities rhodanine-furan conjugates which its application in the different field of drug discovery.

2-Azabicyclo[3.2.1]octane scaffold: synthesis and applications.

2-Azabicyclo[3.2.1]octanes are nitrogen containing heterocycles with significant potential in the field of drug discovery. This core has been applied as key synthetic intermediate in several total synthesis, while their unique structure can make them a challenging scaffold to acquire. This Minireview summarizes the synthetic approaches to access this bicyclic architecture and highlights its presence in the total synthesis of several target molecules.

In Vivo and Clinical Studies of Natural Products Targeting the Hallmarks of Cancer.

Despite more than 200 approved anticancer agents, cancer remains a leading cause of death worldwide due to disease complexity, tumour heterogeneity, drug toxicity, and the emergence of drug resistance. Accordingly, the development of chemotherapeutic agents with higher efficacy, a better safety profile, and the capability of bypassing drug resistance would be a cornerstone in cancer therapy. Natural products have played a pivotal role in the field of drug discovery, especially for the pharmacotherapy of cancer, infectious, and chronic diseases. Owing to their distinctive structures and multiple mechanistic activities, natural products and their derivatives have been utilized for decades in cancer treatment protocols. In this review, we delve into the potential of natural products as anticancer agents by targeting cancer's hallmarks, including sustained proliferative signalling, evading growth suppression, resisting apoptosis and cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. We highlight the molecular mechanisms of some natural products, in vivo studies, and promising clinical trials. This review emphasizes the significance of natural products in fighting cancer and the need for further studies to uncover their fully therapeutic potential.

Advancing the Boundary of Pre-trained Models for Drug Discovery: Interpretable Fine-Tuning Empowered by Molecular Physicochemical Properties.

In the field of drug discovery, a proliferation of pre-trained models has surfaced, exhibiting exceptional performance across a variety of tasks. However, the extensive size of these models, coupled with the limited interpretative capabilities of current fine-tuning methods, impedes the integration of pre-trained models into the drug discovery process. This paper pushes the boundaries of pre-trained models in drug discovery by designing a novel fine-tuning paradigm known as the Head Feature Parallel Adapter (HFPA), which is highly interpretable, high-performing, and has fewer parameters than other widely used methods. Specifically, this approach enables the model to consider diverse information across representation subspaces concurrently by strategically using Adapters, which can operate directly within the model's feature space. Our tactic freezes the backbone model and forces various small-size Adapters' corresponding subspaces to focus on exploring different atomic and chemical bond knowledge, thus maintaining a small number of trainable parameters and enhancing the interpretability of the model. Moreover, we furnish a comprehensive interpretability analysis, imparting valuable insights into the chemical area. HFPA outperforms over seven physiology and toxicity tasks and achieves state-of-the-art results in three physical chemistry tasks. We also test ten additional molecular datasets, demonstrating the robustness and broad applicability of HFPA.

DMAMP: A deep-learning model for detecting antimicrobial peptides and their multi-activities.

Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and their activities as two independent tasks, which ignore the relationship between them. Therefore, the combination and sharing of patterns for two tasks is a crucial problem that needs to be addressed. In this study, we propose a deep learning model, called DMAMP, for detecting AMPs and activities simultaneously, which is benefited from multi-task learning. The first stage is to utilize convolutional neural network models and residual blocks to extract the sharing hidden features from two related tasks. The next stage is to use two fully connected layers to learn the distinct information of two tasks. Meanwhile, the original evolutionary features from the peptide sequence are also fed to the predictor of the second task to complement the forgotten information. The experiments on the independent test dataset demonstrate that our method performs better than the single-task model with 4.28% of Matthews Correlation Coefficient (MCC) on the first task, and achieves 0.2627 of an average MCC which is higher than the single-task model and two existing methods for five activities on the second task. To understand whether features derived from the convolutional layers of models capture the differences between target classes, we visualize these high-dimensional features by projecting into 3D space. In addition, we show that our predictor has the ability to identify peptides that achieve activity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). We hope that our proposed method can give new insights into the discovery of novel antiviral peptide drugs.

The role of silicon in drug discovery: a review.

This review aims to highlight the role of silicon in drug discovery. Silicon and carbon are often regarded as being similar with silicon located directly beneath carbon in the same group in the periodic table. That being noted, in many instances a clear dichotomy also exists between silicon and carbon, and these differences often lead to vastly different physiochemical and biological properties. As a result, the utility of silicon in drug discovery has attracted significant attention and has grown rapidly over the past decade. This review showcases some recent advances in synthetic organosilicon chemistry and examples of the ways in which silicon has been employed in the drug-discovery field.

Crystal structure analysis of helix–turn–helix type motifs in α,γ-hybrid peptides

Design of helix–turn–helix type mimetics using short α,γ-hybrid peptides as helices and (E)-α,β-unsaturated γ-amino acids as conformationally rigid linkers and their conformations in single crystals are reported.

Molecular docking, derivatization, characterization and biological assays of amantadine.

Background: Derivatization has been tremendously utilized in the field of drug discovery for optimizing the pharmacological properties and improving safety, efficacy and selectivity.Methodology: Schiff's bases (AD1-AD11) are synthesized through amantadine condensation with different aldehydes and ketones. Fourier transform infrared, 1H NMR, 13C NMR, TLC, liquid chromatography mass spectrometry analysis, in silico studies, molecular docking and antiviral activity through hemagglutinin test were performed for evaluation of new compounds.Results: AD2, 3 and 9-11 showed greater antiviral activity than the parent drug. Among all derivatives, AD2 and AD3 exhibited good potential against α-amylase while AD7 and AD10 showed stronger inhibition against α-glucosidase.Conclusion: So, it is concluded that the most potent derivatives can be used as lead compounds in novel drug design of antiviral antidiabetic agents.

Repurposing of biologics and biopharmaceuticals.

The field of drug repurposing is gaining attention as a way to introduce pharmaceutical agents with established safety profiles to new patient populations. This approach involves finding new applications for existing drugs through observations or deliberate efforts to understand their mechanisms of action. Recent advancements in bioinformatics and pharmacology, along with the availability of extensive data repositories and analytical techniques, have fueled the demand for novel methodologies in pharmaceutical research and development. To facilitate systematic drug repurposing, various computational methodologies have emerged, combining experimental techniques and in silico approaches. These methods have revolutionized the field of drug discovery by enabling the efficient repurposing of screens. However, establishing an ideal drug repurposing pipeline requires the integration of molecular data accessibility, analytical proficiency, experimental design expertise, and a comprehensive understanding of clinical development processes. This chapter explores the key methodologies used in systematic drug repurposing and discusses the stakeholders involved in this field. It emphasizes the importance of strategic alliances to enhance the success of repurposing existing compounds for new indications. Additionally, the chapter highlights the current benefits, considerations, and challenges faced in the repurposing process, which is pursued by both biotechnology and pharmaceutical companies. Overall, drug repurposing holds great promise in expanding the use of existing drugs and bringing them to new patient populations. With the advancements in computational methodologies and the collaboration of various stakeholders, this approach has the potential to accelerate drug development and improve patient outcomes.

TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

Background and objectiveRecent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. MethodsTo address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. ResultsExperimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. ConclusionsIn this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

Harnessing the power of seaweed: unveiling the potential of marine algae in drug discovery

Seaweeds, also known as marine algae, have gained attention as a promising source of bioactive compounds with potential applications in drug discovery. This review explores the emerging field of seaweed-based drug discovery and highlights the diverse range of bioactive compounds found in seaweeds, including polysaccharides, phlorotannins, pigments, and peptides. These compounds exhibit various pharmacological activities such as antioxidant, anti-inflammatory, antimicrobial, antiviral, and anticancer effects. Seaweeds have demonstrated particular promise in the areas of cancer research, with certain species showing potent antitumor properties. Additionally, their anti-inflammatory, antimicrobial, and neuroprotective potential has captured scientific interest in the treatment of chronic diseases and neurodegenerative disorders. However, challenges related to compound identification, extraction methods, scalability of seaweed cultivation, and understanding the mechanisms of action still need to be addressed. As researchers employ advanced technologies and dive deeper into the chemical composition of seaweeds, the untapped potential of these marine organisms in drug discovery awaits further exploration and holds significant promise for future therapeutic advancements.

Network-Based Methods and Their Applications in Drug Discovery.

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.