Field Of Drug Discovery Research Articles

In – Silico Biological Evaluation of Antifungal Drugs - Molinspiration

The aim of this research work is to evaluate In – Silico Antifungal Drugs, in this work Molinspiration is used to provide predictive tools for essential molecular properties, such as lipophilicity, aqueous solubility and pKa, which are critical in drug discovery and cheminformatics. It operates based on a comprehensive database of chemical structures and employs algorithms to calculate these properties. Molinspiration is a renowned software and informatics company that plays a pivotal role in the pharmaceutical and biotechnology industries. With a primary focus on providing cutting-edge cheminformatics tools and software solutions, Molinspiration has significantly contributed to the field of drug discovery and medicinal chemistry. Molinspiration's software is an indispensable resource for the pharmaceutical and biotechnology sectors.

Cyclovirobuxine D inhibits the growth of osteosarcoma cells through the induction of autophagy flux arrest by promoting lysosomal acidification

Osteosarcoma (OS) is an aggressive primary tumor with the highest incidence in children and adolescents. Natural plant compounds (NPCs) have long been promising resources in the field of antitumor drug discovery because of their high efficacy and low toxicity. Here, the aim of this study is to investigate the potential inhibitory effects of Cyclovirobuxine D (CVB-D), a natural bioactive isolated from the traditional Chinese medicinal herb Huangyang, on OS cells. We showed that CVB-D reduced OS cell growth in vitro and in vivo. Mechanistically, CVB-D inhibited PI3K-AKT-mTOR pathway and initiated autophagy. On the other hand, CVB-D induced lysosomal over-acidification by interacting with the V-type proton ATPase 116 kDa subunit a1 (ATP6V0A1), ultimately leading to autophagy flux arrest which might be related to the inhibitory effect of CVB-D on OS cells. Conclusively, our results propose a potential foundation for CVB-D to be developed into an anti-OS drug and an autophagy inhibitor.

Influence of oligonucleotides structures for separation of diastereomers by capillary electrophoresis method using polyvinylpyrrolidone 1,300,000

In the field of oligonucleotides drug discovery, phosphorothioate (PS) modification has been recognized as an effective tool to overcome the nuclease digestion, and generates 2n of possible diastereomers, where n equals the number of PS linkages. However, it is also well known that differences in drug efficacy and toxicity are caused by differences in stereochemistry of oligonucleotides. Therefore, the development of a high-resolution analytical method that enables stereo discrimination of oligonucleotides is desired. Under this circumstance, capillary electrophoresis (CE) using polyvinylpyrrolidone (PVP) is considered as one of the useful tools for the separation analysis of diastereomers.In this study, we evaluated the several oligonucleotides with the structural diversities in order to understand the separation mechanism of the diastereomers by CE. Especially, five kinds of 2′-moieties were deeply examined by CE with PVP 1,300,000 polymer solution. We found that different trend of the peak shapes and the peak resolution were observed among these oligonucleotides. For example, the better peak resolution was observed in 6 mer PS3-DNA compared to the rigid structure of 6 mer PS3-LNA. As for this reason, the computational simulation revealed that difference of accessible surface area caused by the steric structure of thiophosphate in each oligonucleotide is one of the key attributes to explain the separation of the diastereomers. In addition, we achieved the separation of sixteen peak tops of the diastereomers in 6 mer PS4-DNA, and the complete separation of fifteen diastereomers in 6 mer PS4-RNA. These knowledge for the separation of the diastereomers by CE will be expected to the quality control of the oligonucleotide drugs.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

Natural Products That Contain Higher Homologated Amino Acids.

This review focuses on discussing natural products (NPs) that contain higher homologated amino acids (homoAAs) in the structure as well as the proposed and characterized biosynthesis of these non-proteinogenic amino acids. Homologation of amino acids includes the insertion of a methylene group into its side chain. It is not a very common modification found in NP biosynthesis as approximately 450 homoAA-containing NPs have been isolated from four bacterial phyla (Cyanobacteria, Actinomycetota, Myxococcota, and Pseudomonadota), two fungal phyla (Ascomycota and Basidiomycota), and one animal phylum (Porifera), except for a few examples. Amino acids that are found to be homologated and incorporated in the NP structures include the following ten amino acids: alanine, arginine, cysteine, isoleucine, glutamic acid, leucine, phenylalanine, proline, serine, and tyrosine, where isoleucine, leucine, phenylalanine, and tyrosine share the comparable enzymatic pathway. Other amino acids have their individual homologation pathway (arginine, proline, and glutamic acid for bacteria), likely utilize the primary metabolic pathway (alanine and glutamic acid for fungi), or have not been reported (cysteine and serine). Despite its possible high potential in the drug discovery field, the biosynthesis of homologated amino acids has a large room to explore for future combinatorial biosynthesis and metabolic engineering purpose.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

Change in Osmotic Pressure Influences the Absorption Spectrum of Hemoglobin inside Red Blood Cells.

Absorption spectra of red blood cell (RBC) suspensions are investigated in an osmolarity range in the medium from 200 mOsm to 900 mOsm. Three spectral parameters are used to characterize the process of swelling or shrinkage of RBC-the absorbance at 700 nm, the Soret peak height relative to the spectrum background, and the Soret peak wavelength. We show that with an increase in the osmolarity, the absorbance at 700 nm increases and the Soret peak relative height decreases. These changes are related to the changes in the RBC volume and the resulting increase in the hemoglobin intracellular concentration and index of refraction. Confocal microscopy and flow cytometry measurements supported these conclusions. The maximum wavelength of the Soret peak increases with increasing osmolarity due to changes in the oxygenation state of hemoglobin. Using these spectrum parameters, the process of osmosis in RBCs can be followed in real time, but it can also be applied to various processes, leading to changes in the volume and shape of RBCs. Therefore, we conclude that UV-Vis absorption spectrophotometry offers a convenient, easily accessible, and cost-effective method to monitor changes in RBC, which can find applications in the field of drug discovery and diagnostics of RBC and hemoglobin disorders.

To What Extent does CRISPR/Cas9 Truly Elevate Target Efficiency and Revolutionize Drug Discovery?

The emergence of the CRISPR/Cas9 system has ushered in a new era of genetic editing with vast implications for both therapeutic interventions and drug discovery. This essay explores the extent to which CRISPR/Cas9 elevates target efficiency and revolutionizes drug discovery. While this technology offers unprecedented efficiency, precision, and programmability in genome editing, concerns about off-target effects have arisen. The essay discusses the transformative impact of CRISPR/Cas9 in drug discovery, particularly in cancer research and combating antimicrobial resistance. It also highlights strategies for detecting and mitigating off-target effects, such as in silico prediction tools, experimental methods, and precision-enhancing techniques. Ultimately, CRISPR/Cas9’s potential is undeniable, but its full realization depends on ongoing research and refinement to optimize specificity and revolutionize the field of drug discovery.

Modulating the antibacterial effect of the existing antibiotics along with repurposing drug metformin.

Owing to the extensive prevalence of resistant bacteria to numerous antibiotic classes, antimicrobial resistance (AMR) poses a well-known hazard to world health. As an alternate approach in the field of antimicrobial drug discovery, repurposing the available medications which are also called antibiotic resistance breakers has been pursued for the treatment of infections with antimicrobial resistance pathogens. In this study, we used Haloperidol, Metformin and Hydroxychloroquine as repurposing drugs in in vitro (Antibacterial Antibiotic Sensitivity Test and Minimum Inhibitory Concentration-MIC) and in vivo (Shigellosis in Swiss albino mice) tests in combination with traditional antibiotics (Oxytetracycline, Erythromycin, Doxycycline, Gentamicin, Ampicillin, Chloramphenicol, and Penicillin) against a group of AMR resistance bacteria (Bacillus cereus, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Shigella boydii). After observing the results of the conducted in vitro experiments we studied the effects of the above non antibiotic drugs in combination with the said antibiotics. As an repurposing adjuvant antibiotic drug, Metformin exhibited noteworthy activity in almost all in vitro, in vivo and in silico tests (Zone of inhibition for 30 to 43mm for E.coli in combination with Doxycycline; MIC value decreased 50 µM to 0.781 µM with Doxycycline on S. boydii).In rodents Doxycycline and Metformin showed prominent against Shigellosis in White blood cell count (6.47 ± 0.152 thousand/mm3) and Erythrocyte sedimentation rate (10.5 ± 1.73mm/hr). Our findings indicated that Metformin and Doxycycline combination has a crucial impact on Shigellosis. The molecular docking study was performed targeting the Acriflavine resistance protein B (AcrB) (PDB ID: 4CDI) and MexA protein (PDB ID: 6IOK) protein with Metformin (met8) drug which showed the highest binding energy with - 6.4kcal/mol and - 5.5kcal/mol respectively. Further, molecular dynamics simulation revealed that the docked complexes were relatively stable during the 100 ns simulation period. This study suggest Metformin and other experimented drugs can be used as adjuvants boost up antibiosis but further study is needed to find out the safety and efficacy of this non-antibiotic drug as potent antibiotic adjuvant.

Abstract 4160: V7 targeted liposomes detect pancreatic adenocarcinoma via multispectral optoacoustic tomography

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDA) remains one of the most aggressive malignancies with a 5-year survival rate of 9-10%. Insufficient early detection coupled with limitations in operating surgery renders PDA as one of the most challenging tumors to treat. Accurate localization of tumor margins can tremendously improve surgical treatment outcomes in patients. Recently, nanotechnology has emerged as a promising platform for theranostic applications in the field of drug discovery, although the clinical success is limited by their tumor-specific uptake. Hypoxia due to aberrant vascularization is a hallmark of PDA and can be exploited for active targeting. pH-low insertion peptide variant 7 (V7) is used as hypoxia and acidosis-dependent biomarker for selective targeting of pancreatic cancer tissue. This work is focused on using IR 780 contrast agent loaded, V7 peptide conjugated liposomes for the active targeting of PDA towards enhanced surgical outcomes. Methods: Liposomes encapsulating IR780 dye were prepared using thin film hydration technique. V7 peptide was conjugated to liposomes using an SMCC linker and liposomes were characterized for their size, polydispersity and surface potential. In-vitro uptake of IR780-V7-liposome by S2VP10 at pH 7.4, 6.8, and 6.6 was evaluated through NIR fluorescence imaging using an Odyssey Infrared System. Athymic mice were orthotopically implanted with S2VP10 cells. After tumors were 3 mm, V7-liposomes and untargeted liposomes (0.01 OD) were intravenously injected and biodistribution and tumor specific uptake was accessed using multispectral optoacoustic imaging (MSOT). Results: Dynamic light scattering (DLS) revealed the size of liposomes to be 109 nm with a polydispersity index of 0.198 and zeta potential of +35.8 mV. Surface conjugation of V7 peptide was confirmed using the zeta potential measurement. NIR fluorescence imaging in S2VP10 cells established enhanced tumor specific uptake of V7 conjugated liposomes than passively targeted liposomes (p<0.05). Biodistribution of V7-liposomes demonstrated tumor specific accumulation (PDAC tumor 23.7 a.u, liver 3.6 a.u., and kidney 1.1 a.u.) while untargeted liposomes had the greatest uptake in liver (PDAC tumor 2.1 a.u., liver 6.3 a.u., and kidney 0.7 a.u.) p<0.05. Conclusion: pH-specific actively targeted liposomes can be used to specifically target PDA with minimal off-target binding effects. V7-IR780-liposome formulation can be used to efficiently deliver contrast agent for imaging tumor margins and to expand the potential of MSOT towards clinical applications. Citation Format: Happy Agarwal, Phillip Chuong, William E. Grizzle, Barish H. Edil, Ajay Jain, Lacey R. McNally. V7 targeted liposomes detect pancreatic adenocarcinoma via multispectral optoacoustic tomography [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 4160.

Abstract 3524: Leveraging ChatGPT for literature-based inference of drug-gene relationships in cancer

Abstract Approximately 5,000 new articles are indexed by PubMed daily, many providing critical insights into novel gene-drug relationships. However, searching and distilling the wealth of biomedical literature to infer specific pharmacogenomic relationships between a gene and a drug poses a significant burden for researchers. Recent advances in large language models, such as ChatGPT, offer a promising solution to tackle this challenge. This study aims to prototype a literature-based inference for drug-gene relationships in the context of cancer using ChatGPT, marking an exploratory effort in this emerging domain. Our approach involves developing an automated pipeline that integrates the Application Programming Interfaces (APIs) of PubMed and ChatGPT, specifically GPT-3.5. Given a gene, a drug, and a disease, the pipeline searches PubMed for relevant articles and extracts their abstracts. Leveraging prompt engineering techniques, we formulated a prompt that facilitates accurate summarization of these abstracts. The output of our pipeline includes three key components: 1) a concise, one-sentence summary elucidating the relationship between the drug-gene pair; 2) a step-by-step explanation of the inference process; and 3) the confidence level associated with the generated summary. Our approach was able to confirm well-known gene-drug relationships in cancer, such as palbociclib and CDK4. We further examined a challenging case of CX-5461, an inhibitor of ribosomal RNA synthesis currently under clinical investigation for various cancers. Until recent evidence emerged in 2021, this drug was mischaracterized as an RNA-Pol I inhibitor, whereas it primarily targets topoisomerase II beta (TOP2B). Notably, our pipeline correctly identified TOP2B as the primary target of CX-5461, despite an extensive body of literature prior to 2021 supporting the RNA-Pol I relationship. To evaluate the sensitivity of our pipeline, we conducted a systematic assessment using a mixture of relevant and irrelevant abstracts. Utilizing bevacizumab, VEGF, and hepatocellular carcinoma as a demonstrating example, we showed that ChatGPT achieved nearly 95% accuracy even when only 30% of the abstracts were relevant to the case. In summary, this pilot research serves as a foundational step towards the utilization of large language models in the field of drug discovery and development. Our ongoing efforts involve the rigorous evaluation of our approach across a diverse spectrum of drug targets and cancer types, as well as the optimization of prompts through state-of-the-art prompt engineering techniques. Citation Format: Yuna Shin, Michael Ning, Li-Ju Wang, Yufei Huang, Yu-Chiao Chiu. Leveraging ChatGPT for literature-based inference of drug-gene relationships in cancer [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 3524.

Abstract 3093: Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids

Abstract Although patient-derived tumor organoids (PDOs) better-represent patient tumors compared to traditional in vitro cell cultures, limitations of PDO analysis remain a major hurdle for high-throughput screening (HTS) applications. In particular, current PDO analysis fall short in at least 1 of 2 major criteria: 1) accurate recapitulation of patient tumor or 2) scalability. To address this, we have developed an automation-compatible protocol to satisfy these 2 major criteria to facilitate the adoption of PDOs for HTS applications1. Our novel protocol leverages live-cell imaging techniques to capture the dynamic interactions and intricate complexities of PDOs, which allowed for more precise and comprehensive analysis. To further enhance the accuracy and efficiency of our approach, we developed a fully automated image and data analysis pipeline (Orbits). Using label-free detection of organoids from brightfield images, Orbits integrates growth-rate-based drug response metrics with drug synergy metrics to significantly improve the identification of synergistic drug interactions. Here, present the results of our HTS protocol on 10 distinct drug combination strategies, specifically focusing on the repurposed drug candidate Auranofin, a Thioredoxin reductase inhibitor. We screened on 10 PDOs sourced from healthy lung, non-small cell lung cancer, and pancreatic ductal adenocarcinoma. Not only were we able to distinguishing cytostatic from cytotoxic drug responses at a single-organoid level, we identified drug candidates that can synergistically enhanced the efficacy of Auranofin in a tumor selective manner. Our study presents a major step forward in the field of PDO-based drug discovery, offering a robust protocol for HTS and automated data analysis. The implementation of our method will accelerate the discovery of effective therapeutic strategies, ultimately translating to improved patient outcomes. 1 Le Compte, M., et al JoVE 190 (2022) Citation Format: Abraham Lin, Maxim Le Compte, Tyler Gilcrest, Edgar Cardenas De La Hoz, Geert Roeyen, Jeroen Hendriks, Filip Lardon, Christophe Deben. Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids [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 3093.

LGGA-MPP: Local Geometry-Guided Graph Attention for Molecular Property Prediction.

Molecular property prediction is a fundamental task of drug discovery. With the rapid development of deep learning, computational approaches for predicting molecular properties are experiencing increasing popularity. However, these existing methods often ignore the 3D information on molecules, which is critical in molecular representation learning. In the past few years, several self-supervised learning (SSL) approaches have been proposed to exploit the geometric information by using pre-training on 3D molecular graphs and fine-tuning on 2D molecular graphs. Most of these approaches are based on the global geometry of molecules, and there is still a challenge in capturing the local structure and local interpretability. To this end, we propose local geometry-guided graph attention (LGGA), which integrates local geometry into the attention mechanism and message-passing of graph neural networks (GNNs). LGGA introduces a novel method to model molecules, enhancing the model's ability to capture intricate local structural details. Experiments on various data sets demonstrate that the integration of local geometry has a significant impact on the improved results, and our model outperforms the state-of-the-art methods for molecular property prediction, establishing its potential as a promising tool in drug discovery and related fields.

Repurposing of Compounds from Streptomyces spp. as Potential Inhibitors of Aminoacyltransferase FemA: An Essential Drug Target against Drug-resistant Staphylococcus aureus.

Drug-resistant Staphylococcus aureus represents a substantial healthcare challenge worldwide, and its range of available therapeutic options continues to diminish progressively. Thus, this study aimed to identify potential inhibitors against FemA, a crucial protein involved in the cell wall biosynthesis of S. aureus. The screening process involved a comprehensive structure-based virtual screening on the StreptomDB database to identify ligands with potential inhibitory effects on FemA using AutoDock Vina. The most desirable ligands with the highest binding affinity and pharmacokinetic properties were selected. Two ligands with the highest number of hydrogen bonds and hydrophobic interactions were further analyzed by molecular dynamics (MD) using the GROMACS version 2018 simulation package. Six H-donor conserved residues were selected as protein active sites, including Arg- 220, Tyr-38, Gln-154, Asn-73, Arg-74, and Thr-24. Through virtual screening, a total of nine compounds with the highest binding affinity to the FemA protein were identified. Frigocyclinone and C21H21N3O4 exhibited the highest binding affinity and demonstrated favorable pharmacokinetic properties. Molecular dynamics analysis of the FemA-ligand complexes further indicated desirable stability and reliability of complexes, reinforcing the potential efficacy of these ligands as inhibitors of FemA protein. Our findings suggest that Frigocyclinone and C21H21N3O4 are promising inhibitors of FemA in S. aureus. To further validate these computational results, experimental studies are planned to confirm the inhibitory effects of these compounds on various S. aureus strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.

Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK.

Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag.

RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.

Tensor improve equivariant graph neural network for molecular dynamics prediction

Molecular dynamics(MD) simulations are essential for molecular structure optimization, drug-drug interactions, and other fields of drug discovery by simulating the motion of microscopic particles to calculate their macroscopic properties (e.g., energy). The main problems of the existing work are as follows: (1) Failure to fully consider the chemical bonding constraints between atoms, (2) Group equivariance can help achieve robust and accurate predictions of MD under arbitrary reference transformations and should be incorporated into the model design, (3) Tensor information such as relative position, velocity, and torsion angle can be used to enhance the prediction of molecular dynamics. And the existing methods are mainly limited to the scalar domain.In this paper, we propose a new model—tensor improve equivariant graph neural network for molecular dynamics prediction (TEGNN): (1) The model materialization of chemical bond constraints between atoms into geometric constraints. The molecule’s forward kinematic information (position and velocity) is represented by generalized coordinates. In this way, the interatomic chemical bonding constraints are implicitly and naturally encoded in the forward kinematics, (2) The equivariant information transfer is allowed in TEGNN, which significantly improves the accuracy and computational efficiency of the final prediction, (3) TEGNN introduces equivariant locally complete frames into scalar-only equivariant graph neural networks, thus allowing the projection of tensor information of a given order onto the frame. On the N-body dataset of simulated molecular systems consisting of particles, sticks, and hinges, as well as on the real dataset MD17 for molecular dynamics prediction, multiple experiments support that TEGNN is ahead of the current state-of-the-art GNN in terms of prediction accuracy, constraint satisfaction, and data efficiency.We extend the current state-of-the-art equivariant neural network model. The proposed TEGNN accommodates more tensor information and considers the chemical bonding constraints between atoms during motion, ultimately improving the performance of predicting the kinematic states of molecules.

CHL-DTI: A Novel High-Low Order Information Convergence Framework for Effective Drug-Target Interaction Prediction.

Recognizing drug-target interactions (DTI) stands as a pivotal element in the expansive field of drug discovery. Traditional biological wet experiments, although valuable, are time-consuming and costly as methods. Recently, computational methods grounded in network learning have demonstrated great advantages by effective topological feature extraction and attracted extensive research attention. However, most existing network-based learning methods only consider the low-order binary correlation between individual drug and target, neglecting the potential higher-order correlation information derived from multiple drugs and targets. High-order information, as an essential component, exhibits complementarity with low-order information. Hence, the incorporation of higher-order associations between drugs and targets, while adequately integrating them with the existing lower-order information, could potentially yield substantial breakthroughs in predicting drug-target interactions. We propose a novel dual channels network-based learning model CHL-DTI that converges high-order information from hypergraphs and low-order information from ordinary graph for drug-target interaction prediction. The convergence of high-loworder information in CHL-DTI is manifested in two key aspects. First, during the feature extraction stage, the model integrates both high-level semantic information and low-level topological information by combining hypergraphs and ordinary graph. Second, CHL-DTI fully fuse the innovative introduced drug-protein pairs (DPP) hypergraph network structure with ordinary topological network structure information. Extensive experimentation conducted on three public datasets showcases the superior performance of CHL-DTI in DTI prediction tasks when compared to SOTA methods. The source code of CHL-DTI is available at https://github.com/UPCLyy/CHL-DTI .

High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line

BackgroundThe development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations.MethodsTo address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses.ResultsBy examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails.ConclusionsIn conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.