Structural designing of suppressors for autisms spectrum diseases using molecular dynamics sketch

In this paper we are sketching the chemical structure of suppressor drug for autism spectrum disorder using a computational tool. Here we are designing three molecular compounds like Fluoxetine, Risperidone, Melatonin. Structuring the suppressors, sketching the aromatization and bonding of the functional groups with the elements like Oxygen, Nitrogen, halogens. In our work we are using computational algorithm for drawing the structure of suppressor drug. In this paper we are mentioning the autism spectrum suppressor’s molecular formula as well as structural formula

Novel drug-target interactions via link prediction and network embedding

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04650-w

Phenylpyrazalopyrimidines as Tyrosine Kinase Inhibitors: Synthesis, Antiproliferative Activity, and Molecular Simulations

N1-(α,β-Alkene)-substituted phenylpyrazolopyrimidine derivatives with acetyl and functionalized phenyl groups at α- and β-positions, respectively, were synthesized by the reaction of 3-phenylpyrazolopyrimidine (PhPP) with bromoacetone, followed by a chalcone reaction with differently substituted aromatic aldehydes. The Src kinase enzyme assay revealed modest inhibitory activity (half maximal inhibitory concentration, IC50 = 21.7–192.1 µM) by a number of PhPP derivatives. Antiproliferative activity of the compounds was evaluated on human leukemia (CCRF-CEM), human ovarian adenocarcinoma (SK-OV-3), breast carcinoma (MDA-MB-231), and colon adenocarcinoma (HT-29) cells in vitro. 4-Chlorophenyl carbo-enyl substituted 3-phenylpyrazolopyrimidine (10) inhibited the cell proliferation of HT-29 and SK-OV-3 by 90% and 79%, respectively, at a concentration of 50 µM after 96 h incubation. The compound showed modest inhibitory activity against c-Src (IC50 = 60.4 µM), Btk (IC50 = 90.5 µM), and Lck (IC50 = 110 µM), while it showed no activity against Abl1, Akt1, Alk, Braf, Cdk2, and PKCa. In combination with target selection and kinase profiling assay, extensive theoretical studies were carried out to explore the selectivity behavior of compound 10. Specific interactions were also explored by examining the changing trends of interactions of tyrosine kinases with the phenylpyrazolopyrimidine derivative. The results showed good agreement with the experimental selectivity pattern among c-Src, Btk, and Lck

Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

Machine Learning Applications for Drug Repurposing

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected until a very late stage of the drug discovery, where the discovery of drug-induced side effects and potential drug resistance can decrease the value of the drug and even completely invalidate the use of the drug. Thus, a new paradigm in drug discovery is needed. Structural systems pharmacology is a new paradigm in drug discovery that the drug activities are studied by data-driven large-scale models with considerations of the structures and drugs. Structural systems pharmacology will model, on a genome scale, the energetic and dynamic modifications of protein targets by drug molecules as well as the subsequent collective effects of drug-target interactions on the phenotypic drug responses. To date, however, few experimental and computational methods can determine genome-wide protein-ligand interaction networks and the clinical outcomes mediated by them. As a result, the majority of proteins have not been charted for their small molecular ligands; we have a limited understanding of drug actions. To address the challenge, this dissertation seeks to develop and experimentally validate innovative computational methods to infer genome-wide protein-ligand interactions and multi-scale drug-phenotype associations, including drug-induced side effects. The hypothesis is that the integration of data-driven bioinformatics tools with structure-and-mechanism-based molecular modeling methods will lead to an optimal tool for accurately predicting drug actions and drug associated phenotypic responses, such as side effects. This dissertation starts by reviewing the current status of computational drug discovery for complex diseases in Chapter 1. In Chapter 2, we present REMAP, a one-class collaborative filtering method to predict off-target interactions from protein-ligand interaction network. In our later work, REMAP was integrated with structural genomics and statistical machine learning methods to design a dual-indication polypharmacological anticancer therapy. In Chapter 3, we extend REMAP, the core method in Chapter 2, into a multi-ranked collaborative filtering algorithm, WINTF, and present relevant mathematical justifications. Chapter 4 is an application of WINTF to repurpose an FDA-approved drug diazoxide as a potential treatment for triple negative breast cancer, a deadly subtype of breast cancer. In Chapter 5, we present a multilayer extension of REMAP, applied to predict drug-induced side effects and the associated biological pathways. In Chapter 6, we close this dissertation by presenting a deep learning application to learn biochemical features from protein sequence representation using a natural language processing method

The anti-proliferative activity of drimia altissima and a novel isolated flavonoid glycoside against hela cervical cancer cells

Cancer is one of the leading causes of mortality worldwide. About 44% of all cancer morbidity and 53% of all cancer mortality occur in countries with a low to medium Human Development Index (HDI). Thus, cancer is rapidly emerging as a serious threat to public health in Africa and most especially, sub-Saharan Africa. The International Agency for Research on Cancer (IARC) projects that there will be 1.28 million new cancer cases and 970 000 cancer deaths in Africa by the year 2030 owing to the increase in economic development associated lifestyles. The dominant types of cancer in Africa are those related to infectious diseases such as Kaposi’s sarcoma and cervical, hepatic and urinary bladder carcinomas. The main challenge to cancer treatment in Africa is the unavailability of efficacious anticancer drugs. This is because most developing countries can only afford to procure the most basic anticancer drugs, which are also frequently unavailable due to intermittent supplies. This results in patients progressing to more advanced cancer states. One way of combating this African problem is to focus on research that aims at discovering efficacious and cost effective cancer therapies from available natural resources within the African continent. This study investigated the potential anti-proliferative activity (against HeLa cervical cancer cells) of four plants (Adansonia digitata, Ceiba pentandra, Maytenus senegalensis and Drimia altissima) commonly used in the African traditional treatment of malignancies. After in vitro bio-assay screening using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay, M. senegalensis root extract (MS-R) and D. altissima bulb extract (DA-B) showed anti-proliferative activity against HeLa cervical cancer cells with IC50 values of 25 μg/mL and 1.1 μg/mL respectively. By possessing the strongest anti-proliferative activity among the tested extracts, D. altissima was selected for further studies. Liquid-liquid partitioning of the Drimia altissima bulb extract with n-hexane, ethyl acetate, and n-butanol, yielded partitions 79a – d, with the n-butanol fraction, 79d, exhibiting the strongest cytotoxic activity (IC50 = 0.497 μg/mL). Through High Content Analysis (HCA) screening, fraction 79d was found to induce marked early mitotic cell cycle arrest. Fractionation of 79d using Diaion® HP-20 open column chromatography and a stepwise gradient of reducing polarity (water-methanol-ethanol-ethyl acetate) yielded cytotoxic fractions 82b, 82c, 82d and 82e, all with significant anti-proliferative activities at the tested concentrations of 0.1, 1.0 and 10 μg/mL. Bio-assay guided fractionation of 82c (the most effective fraction at the lowest tested concentration of 0.1 μg/mL) using Sephadex® LH-20 open column chromatography and 50% MeOH led to the isolation of compound 3.17. After structural elucidation using 1D and 2D Nuclear Magnetic Resonance spectroscopy (NMR), High resolution Mass spectrometry (HRMS), Fourier-Transform Infrared spectroscopy (FT-IR), ultraviolet spectroscopy (UV) and Circular Dichroism (CD), compound 3.17 was identified as a novel C-glucosylflavonoid-O-glucoside, 6-C-[-apio-α-D-furanosyl-(1→6)-β-glucopyranosyl]-4′, 5, 7-trihydroxyflavone (Altissimin, 3.17). Compound 3.17 exhibited a dose dependant anti-proliferative activity with an IC50 of 2.44 μM. The mechanism of action for compound 3.17 was investigated through cell cycle arrest, phosphatidylserine translocation (PS), caspase activation and mitochondrial membrane depolarization. The mechanism of cell death elicited by compound 3.17 in HeLa cells was found to involve the induction of M phase cell cycle arrest with consequent activation of apoptotic cell death which was evident from annexin V staining, mitochondrial membrane potential (ΔΨm) collapse and the activation of caspases -8 and -3. In silico computational techniques were employed to virtually determine potential biological targets of compound 3.17. Target fishing using the Similarity Ensemble Approach (SEA) target prediction gave human aldose reductase (hAR, AKR1B1) the highest ranking with a p value of 2.85 x 10-24, a max Tc of 0.35 and a Z-score of 41.8217. Using AutoDock4 and the AutoDock tools suite (ADT), molecular docking of compound 3.17 in the hAR binding pocket was successfully achieved with a lower ΔG free energy binding (-9.4 kcal/mol) than that of positive control ligand 393 (-8.7 kcal/mol). In conclusion, this study identified the genus Drimia and particularly D. altissima as a potential source for novel cytotoxic compounds. The discovery of altissimin (3.17), the first flavonoid glycoside to be isolate from D. altissima, enquires into the possible existence of similar compounds within the species. In addition to the observed in vitro cytotoxic activity against HeLa cells, the potential of altissimin (3.17) as a hAR enzyme inhibitor opens up the possibility of its use as an adjunct to increase cancer cell sensitivity to chemotherapy. Thus, altissimin (3.17) shows promise as a potential anticancer agent