Drug Discovery Research Articles

Augmenting large language models with chemistry tools

AbstractLarge language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow’s effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

Prediction of Drug-Target Affinity Using Attention Neural Network

Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

From Protein Structures to Functional Biomimetics

AbstractThe development of complex molecular scaffolds with defined folding properties represents a central challenge in chemical research. Proteins are natural scaffolds defined by a hierarchy of structural complexity and have evolved to manifest unique functional characteristics; for example, molecular recognition capabilities that facilitate the binding of target molecules with high affinity and selectivity. Utilizing these features, proteins have been used as a starting point for the design of synthetic foldamers and enhanced biocatalysts, as well as bioactive reagents in drug discovery. In this account, we describe the strategies used in our group to stabilize protein folds, ranging from the constraint of bioactive peptide conformations to chemical protein engineering. We discuss the evolution of peptides into peptidomimetics to inhibit protein–protein and protein–nucleic acid interactions, and the selective chemical modification of proteins to enhance their properties for biotechnological applications. The reported peptide- and proteomimetic structures cover a broad range of molecular sizes and they highlight the importance of structure stabilization for the design of functional biomimetics.1 Introduction2 Constraining the Conformation of Peptides3 Peptide-Based Covalent Protein Modifiers4 Chemical Protein Engineering5 Conclusions

Computational chemistry review article

In the ever-evolving realm of chemistry, the challenges of experimental procedures, including high costs and time constraints, have necessitated the exploration of alternative methodologies. Computational Chemistry, underpinned by algorithms, physical theories, and artificial intelligence (AI), has emerged as a promising avenue, offering insights into molecular structures and interactions without the need for physical experiments. This review delves into the intricacies of Computational Chemistry, highlighting its advantages over traditional experimental methods, especially in the context of the EGFR genome and drug preparation. Furthermore, the principles of molecular dynamics simulations, rooted in Newtons second law, are elucidated, emphasizing the pivotal role of force fields in simulating molecular behaviors. The application spectrum of molecular dynamics, from drug discovery to material design, is explored, showcasing the transformative potential of integrating AI in these domains. The synergy between AI and molecular dynamics promises a future where molecular behaviors are understood with unprecedented depth and speed, paving the way for rapid innovations in drug discovery, material design, and beyond.

How Much Potential Do Nucleoside Analogs Offer to Combat Human Corona Viruses?

Nucleoside analogs (NAs) have been extensively examined as plausible antiviral agents in recent years, in particular since the outbreak of the global pandemic of COVID-19 in 2019. In this review, the structures and antiviral properties of over 450 NAs are collected according to the type of virus, namely SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-229E, and HCoV-NL63. The activity of the NAs against HCoV-related enzymes is also presented. Selected studies dealing with the mode of action of the NAs are discussed in detail. The repurposing of known NAs appears to be the most extensively investigated scientific approach towards efficacious anti-HCoV agents. The recently reported de novo-designed NAs seem to open up additional approaches to new drug candidates.

Piroxicam analogue metal complexes: Synthesis, crystal structures, molecular modeling and biological studies

In recent decades, metal‐based drugs have gained considerable attention all over the world because of their enhanced therapeutic potential. The piroxicam analogue, that is, 2‐Benzyl‐4‐hydroxy‐1,1‐dioxo‐1,2‐dihydro‐1λ6‐benzo [e][1,2] thiazine‐3‐carboxylic acid pyridin‐2‐yl amide 1 was synthesized and coupled with Mn (II), Co (II), Ni (II), Cu (II) and Zn (II) metal ions to isolate complexes 2–6. The N,O‐bidentate chelator coordinated with the metal center via pyridyl nitrogen and amide oxygen atoms by forming six‐membered ring system. Several analytical techniques like UV–Vis, FT‐IR, 1H NMR, magnetic susceptibility, and elemental analysis were performed to confirm the syntheses of the octahedral complexes. The conductance measurement was employed to determine the non‐electrolytic nature of the complexes. The molecular structures of manganese (II) 2, cobalt (II) 3, and zinc (II) 6, complexes were confirmed by single beam X‐ray crystallography revealing the monoclinic crystal system with P21/c space group. The crystallographic data was utilized to carry out the DFT studies, which were found in line with the experimental data. The antioxidant (DPPH, FRAP) and antibacterial activities were performed to determine the therapeutic potential of these synthesized compounds. These studies indicated that understudied compounds have potential as therapeutic agents and they may be used as drug candidates in the future. The in‐silico studies also aligned with the in‐vitro potential of the targeted compounds.

The workshops on computational applications in secondary metabolite discovery (CAiSMD)

Abstract We report the outcomes of the second session of the free online open-access workshop “Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2022” that took place from 09 to 11 March 2022. The first session was held from 08 to 10 March 2021 and drew the attention of many early career scientists from academia and industry. The 23 invited speakers of this year’s workshop also came from academia and industry and 222 registered participants from five continents (Africa, Asia, Europe, South, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites or natural products as drug candidates and drug leads. For three days, the participants of this online workshop discussed modern computer-based approaches for exploring NP discovery in the “omics” age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. These were followed by oral presentations during which much interaction between the speakers and the audience was observed. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) upon submission of an abstract. The final program available on the workshop website (https://indiayouth.info/index.php/caismd) comprised three keynote lectures, 14 oral presentations, two round table discussions, and four hands-on sessions. This meeting report also references internet resources for computational biology around secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community.

Screening Splice-Switching Antisense Oligonucleotides in Pancreas-Cancer Organoids.

Aberrant alternative splicing is emerging as a cancer hallmark and a potential therapeutic target. It is the result of dysregulated or mutated splicing factors, or genetic alterations in splicing-regulatory cis-elements. Targeting individual altered splicing events associated with cancer-cell dependencies is a potential therapeutic strategy, but several technical limitations need to be addressed. Patient-derived organoids are a promising platform to recapitulate key aspects of disease states, and to facilitate drug development for precision medicine. Here, we report an efficient antisense-oligonucleotide (ASO) lipofection method to systematically evaluate and screen individual splicing events as therapeutic targets in pancreatic ductal adenocarcinoma organoids. This optimized delivery method allows fast and efficient screening of ASOs, e.g., those that reverse oncogenic alternative splicing. In combination with advances in chemical modifications of oligonucleotides and ASO-delivery strategies, this method has the potential to accelerate the discovery of antitumor ASO drugs that target pathological alternative splicing.

Evaluation of in vitro antioxidant activities, total phenolic and elemental contents of common herbs and spices (Moringa oleifera leaves, Allium sativum (Garlic) and Momordica charantia (ejinrin) leaves) in South-West Nigeria

Abstract Natural remedies are being widely utilized in some disease’s conditions including cancer, diabetes, neurodegenerative diseases, atherosclerosis hypertension and other cardiovascular diseases. The therapeutic intervention of medicinal plants and spices cannot be overemphasized in the management and control of diseases but the exact modes of action of these herbs and plants have not been fully elucidated. This research work was designed to study the expression of selected elements, zinc (Zn), iron (Fe), copper (Cu), selenium (Se), lead (Pb) and cadmium (Cd); the phytochemical and the in vitro antioxidant properties of ethanolic extracts of Moringa oleifera leaves, Allium sativum (Garlic) and Momordica charantia (ejinrin) leaves for their potential involvement in the prevention and management of cardiovascular diseases and cancer through different chemical methods. In vitro antioxidant properties were assayed by investigating 2,2-diphenyl-2-picryl-hydrazyl (DPPH) free radicals scavenging potentials and Ferric Reducing Antioxidant Potential (FRAP); total phenolic content was determined by using Folin-Ciocalteu assay and the elemental contents of these extracts was investigated through the use of Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). All tests were run in triplicates and analysed. Our results revealed that the ethanolic extracts of M. oleifera leaves, A. sativum (Garlic) and M. charantia (ejinrin) leaves possess significant antioxidant activities and express important and beneficial elements (in marginal and trace amounts) and phytochemicals. These results suggest that the medicinal attributes of these plants may be linked to the radical scavenging abilities, beneficial elements and phytochemicals expression of their ethanolic extracts which may likely be a good direction in the area of drug discovery and development.

Innovative On-DNA Synthesis of Sulfides and Sulfoximines: Enriching the DEL Synthesis Toolbox.

DNA-encoded library (DEL) technologies enable the fast exploration of gigantic chemical space to identify ligands for the target protein of interest and have become a powerful hit finding tool for drug discovery projects. However, amenable DEL chemistry is restricted to a handful of reactions, limiting the creativity of drug hunters. Here, we describe a new on-DNA synthetic pathway to access sulfides and sulfoximines. These moieties, usually contemplated as challenging to achieve through alkylation and oxidation, can now be leveraged in routine DEL selection campaigns.

Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents.

In preclinical studies, high-throughput positron emission tomography (PET) imaging, known as simultaneous multiple animal scanning, can reduce the time spent on animal experiments, the cost of PET tracers, and the risk of synthesis of PET tracers. It is well known that the image quality acquired by high-throughput imaging depends on the PET system. Herein, we investigated the influence of large field of view (FOV) PET scanner on high-throughput imaging. We investigated the influence of scanning four objects using a small animal PET scanner with a large FOV. We compared the image quality acquired by four objects scanned with the one acquired by one object scanned using phantoms and animals. We assessed the image quality with uniformity, recovery coefficient (RC), and spillover ratio (SOR), which are indicators of image noise, spatial resolution, and quantitative precision, respectively. For the phantom study, we used the NEMA NU 4-2008 image quality phantom and evaluated uniformity, RC, and SOR, and for the animal study, we used Wistar rats and evaluated the spillover in the heart and kidney. In the phantom study, four phantoms had little effect on imaging quality, especially SOR compared with that for one phantom. In the animal study as well, four rats had little effect on spillover from the heart muscle and kidney cortex compared with that for one rat. This study demonstrated that an animal PET scanner with a large FOV was suitable for high-throughput imaging. Thus, the large FOV PET scanner can support drug discovery and bridging research through rapid pharmacological and pathological evaluation.

Comprehensive analysis of genomic variation, pan-genome and biosynthetic potential of Corynebacterium glutamicum strains.

Corynebacterium glutamicum is a non-pathogenic species of the Corynebacteriaceae family. It has been broadly used in industrial biotechnology for the production of valuable products. Though it is widely accepted at the industrial level, knowledge about the genomic diversity of the strains is limited. Here, we investigated the comparative genomic features of the strains and pan-genomic characteristics. We also observed phylogenetic relationships among the strains based on average nucleotide identity (ANI). We found diversity between strains at the genomic and pan-genomic levels. Less than one-third of the C. glutamicum pan-genome consists of core genes and soft-core genes. Whereas, a large number of strain-specific genes covered about half of the total pan-genome. Besides, C. glutamicum pan-genome is open and expanding, which indicates the possible addition of new gene families to the pan-genome. We also investigated the distribution of biosynthetic gene clusters (BGCs) among the strains. We discovered slight variations of BGCs at the strain level. Several BGCs with the potential to express novel bioactive secondary metabolites have been identified. Therefore, by utilizing the characteristic advantages of C. glutamicum, different strains can be potential applicants for natural drug discovery.

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods

In this work we investigated the Pks13-TE domain, which plays a critical role in the viability of the mycobacteria. In this report, we have used a series of AI and Physics-based tools to identify Pks13-TE inhibitors. The Reinvent 4, pKCSM, KDeep, and SwissADME are AI-ML-based tools. AutoDock Vina, PLANTS, MDS, and MM-GBSA are physics-based methods. A combination of these methods yields powerful support in the drug discovery cycle. Known inhibitors of Pks13-TE were collected, curated, and used as input for the AI-based tools, and Mol2Mol molecular optimisation methods generated novel inhibitors. These ligands were filtered based on physics-based methods like molecular docking and molecular dynamics using multiple tools for consensus generation. Rigorous analysis was performed on the selected compounds to reduce the chemical space while retaining the most promising compounds. The molecule interactions, stability of the protein-ligand complexes and the comparable binding energies with the native ligand were essential factors for narrowing the ligands set. The filtered ligands from docking, MDS, and binding energy colocations were further tested for their ADMET properties since they are among the essential criteria for this series of molecules. It was found that ligands Mt1 to Mt6 have excellent predicted pharmacokinetic, pharmacodynamic and toxicity profiles and good synthesisability.

Enantioselective construction of stereogenic-at-sulfur(IV) centres via catalytic acyl transfer sulfinylation.

Chiral sulfur pharmacophores are crucial for drug discovery in bioscience and medicinal chemistry. While the catalytic asymmetric synthesis of sulfoxides and sulfinate esters with stereogenic-at-sulfur(IV) centres is well developed, the synthesis of chiral sulfinamides remains challenging, which has primarily been attributed to the high nucleophilicity and competing reactions of amines. In this study, we have developed an efficient methodology for the catalytic asymmetric synthesis of chiral sulfinamides and sulfinate esters by the sulfinylation of diverse nucleophiles, including aromatic amines and alcohols, using our bifunctional chiral 4-arylpyridine N-oxides as catalysts. The remarkable results are a testament to the efficiency, versatility and broad applicability of the developed synthetic approach, serving as a valuable tool for the synthesis of sulfur pharmacophores. Mechanistic experiments and density functional theory calculations revealed that the initiation and stereocontrol of this reaction are induced by an acyl transfer catalyst. Our research provides an efficient approach for the construction of optically pure sulfur(IV) centres.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

The Edifice of Vasculature-On-Chips: A Focused Review on the Key Elements and Assembly of Angiogenesis Models.

The conception of vascularized organ-on-a-chip models provides researchers with the ability to supply controlled biological and physical cues that simulate the in vivo dynamic microphysiological environment of native blood vessels. The intention of this niche research area is to improve our understanding of the role of the vasculature in health or disease progression in vitro by allowing researchers to monitor angiogenic responses and cell-cell or cell-matrix interactions in real time. This review offers a comprehensive overview of the essential elements, including cells, biomaterials, microenvironmental factors, microfluidic chip design, and standard validation procedures that currently govern angiogenesis-on-a-chip assemblies. In addition, we emphasize the importance of incorporating a microvasculature component into organ-on-chip devices in critical biomedical research areas, such as tissue engineering, drug discovery, and disease modeling. Ultimately, advances in this area of research could provide innovative solutions and a personalized approach to ongoing medical challenges.

Exploring the therapeutic potential of Cynanchum tunicatum (Retz.) Alston- assessment of phytochemicals and biological activities

Cynanchum tunicatum (Retz.) Alston is native to the Asian region and is distributed in the tropical areas of India and Sri Lanka. The aim of this study is to explore the phytochemical composition, antioxidant potential, and anti-inflammatory properties of C. tunicatum. Metabolic profiling was carried out using phytochemical screening to detect and quantify the secondary metabolites. To evaluate the potential secondary metabolites using the standard methods, Fourier transform infrared (FTIR), High-Performance Thin Layer Chromatography (HPTLC) and Gas Chromatography–Mass Spectrometry (GC–MS) from organic extracts of C. tunicatum and its biological activities. FTIR investigated peaks that represent alkane and aromatic compounds. GC–MS revealed the presence of 22 constituents such as 1-Hexacosene (0.145 %), l-(+)-Ascorbic acid 2,6-dihexadecanoate (8.129 %), Campesterol (5.243 %), Beta −Amyrin (10.614 %), Lupeol (13.061 %), Octadecanoic acid (0.751 %) are the major active compounds present. HPTLC fingerprinting confirms the bioactive compounds such as colchicine, strychnine, coumarin etc. which are represented with corresponding Rf values. Among all the extracts of C. tunicatum methanolic extract showed highest antioxidant activities. In 2,2-diphenylpicrylhydrazyl method exhibit the IC50 of (38.91 µg/mL), Ferric reducing antioxidant power assay (1.6 µg/mL), total antioxidant assay (IC50 = 32.91 µg/mL) and IC50 of anti-inflammatory activity (42.31 µg/mL) respectively. These findings enrich the knowledge of the species Cynanchum tunicatum for the possible application as a source of bioactive compounds in drug discovery.

AUTOMATION IN ANALYTICAL CHEMISTRY: THE ROLE OF AI IN CHROMATOGRAPHY

Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.

Synthesis of novel coumarin-triazole hybrids and first evaluation of the 4-phenyl substituted hybrid loaded PLGA nanoparticles delivery system to the anticancer activity

Despite the discovery of many chemotherapeutic drugs that prevent uncontrolled cell division processes in the last century, many studies are still being carried out to develop drugs with higher anticancer efficacy and lower level of side effects. Herein, we designed, synthesized, and characterized six novel coumarin-triazole hybrids, and evaluated for anticancer activity of the one with the highest potential against the breast cancer cell line, MCF-7 and human cervical cancer cell line, human cervical adenocarcinoma (HeLa). Compound 21 which was the coumarin derivative including phenyl substituent with the lowest IC50 value displayed the highest cytotoxicity against the studied cancer cell line. Furthermore, the potential use of poly (lactic-co-glycolic acid) nanoparticles (PLGA NPs) prepared by the emulsifying solvent evaporation method as a platform for a drug delivery system was studied on a selected coumarin derivative 21. This coumarin derivative-loaded PLGA NPs were produced with an average size of 225.90 ± 2.96 nm, −16.90 ± 0.85 mV zeta potential, and 4.12 ± 0.90% drug loading capacity. The obtained 21-loaded PLGA nanoparticles were analyzed spectroscopically and microscopically with FT-IR, UV–vis, and scanning electron microscopy as well as thermogravimetric analysis, Raman, and x-ray diffraction. The in vitro release of 21 from the nanoparticles exhibited a controlled release profile just over one month following a burst release in the initial six hours and in addition to this a total release ratio of %50 and %85 were obtained at pH 7.4 and 5.5, respectively. 21-loaded PLGA nanoparticles displayed remarkably effective anticancer activity than 21. The IC50 values were determined as IC50 (21-loaded PLGA nanoparticles): 0.42 ± 0.01 mg ml−1 and IC50 (free 21 molecule): 5.74 ± 3.82 mg ml−1 against MCF-7 cells, and as IC50 (21-loaded PLGA nanoparticles): 0.77 ± 0.12 mg ml−1 and IC50 (free 21 molecule): 1.32 ± 0.31 mg ml−1 against HeLa cells after the incubation period of 24 h. Our findings indicated that triazole-substituted coumarins may be used as an anticancer agent by integrating them into a polymeric drug delivery system providing improved drug loading and effective controlled drug release.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. Datasets and source codes are available at https://github.com/Biohang/DiSMVC. Supplementary data are available at Bioinformatics online.