dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

piscesCSM: prediction of anticancer synergistic drug combinations

While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.

Data-Driven Approaches to Predict Dendrimer Cytotoxicity

Dendrimers are employed as functional elements in contrast agents and are proposed as nontoxic vehicles for drug delivery. Toxicity is a property that is to be evaluated for this novel class of bionanomaterials for in vivo applications. The current research is hampered due to the lack of structured data sets for toxicity studies for dendrimers. In this work, we have built a data set by curating literature for toxicity data and augmented it with structural and physicochemical features. We present a comprehensive, feature-rich database of dendrimer toxicity measured across various cell lines for prediction, design, and optimization studies. We have also explored novel computational approaches for predicting dendrimer cytotoxicity. We demonstrate superior outcomes for toxicity prediction using essential regression in the space of small data sets.

Peptide Dendrimer-Based Antibacterial Agents: Synthesis and Applications

Pathogenic bacteria cause the deaths of millions of people every year. With the development of antibiotics, hundreds and thousands of people's lives have been saved. Nevertheless, bacteria can develop resistance to antibiotics, rendering them insensitive to antibiotics over time. Peptides containing specific amino acids can be used as antibacterial agents; however, they can be easily degraded by proteases in vivo. To address these issues, branched peptide dendrimers are now being considered as good antibacterial agents due to their high efficacy, resistance to protease degradation, and low cytotoxicity. The ease with which peptide dendrimers can be synthesized and modified makes them accessible for use in various biological and nonbiological fields. That is, peptide dendrimers hold a promising future as antibacterial agents with prolonged efficacy without bacterial resistance development. Their in vivo stability and multivalence allow them to effectively target multi-drug-resistant strains and prevent biofilm formation. Thus, it is interesting to have an overview of the development and applications of peptide dendrimers in antibacterial research, including the possibility of employing machine learning approaches for the design of AMPs and dendrimers. This review summarizes the synthesis and applications of peptide dendrimers as antibacterial agents. The challenges and perspectives of using peptide dendrimers as the antibacterial agents are also discussed.

AI-Driven Enhancements in Drug Screening and Optimization

The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.

Molecularly or atomically precise nanostructures for bio-applications: how far have we come?

A huge variety of nanostructures are promising for biomedical applications, but only a few have been practically applied. Among the various reasons, the limited structural preciseness is a critical one, as it increases the difficulty in product quality control, accurate dosing, and ensuring the repeatability of material performance. Constructing nanoparticles with molecule-like preciseness is becoming a new research field. In this review, we focus on the artificial nanomaterials that can currently be molecularly or atomically precise, including DNA nanostructures, some metallic nanoclusters, dendrimer nanoparticles and carbon nanostructures, describing their syntheses, bio-applications and limitations, in view of up-to-date studies. A perspective on their potential for clinical translation is also given. This review is expected to provide a particular rationale for the future design of nanomedicines.

Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence

Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.

Development of Antiepileptic Drugs throughout History: From Serendipity to Artificial Intelligence

This article provides a comprehensive narrative review of the history of antiepileptic drugs (AEDs) and their development over time. Firstly, it explores the significant role of serendipity in the discovery of essential AEDs that continue to be used today, such as phenobarbital and valproic acid. Subsequently, it delves into the historical progression of crucial preclinical models employed in the development of novel AEDs, including the maximal electroshock stimulation test, pentylenetetrazol-induced test, kindling models, and other animal models. Moving forward, a concise overview of the clinical advancement of major AEDs is provided, highlighting the initial milestones and the subsequent refinement of this process in recent decades, in line with the emergence of evidence-based medicine and the implementation of increasingly rigorous controlled clinical trials. Lastly, the article explores the contributions of artificial intelligence, while also offering recommendations and discussing future perspectives for the development of new AEDs.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors

Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer-aided drug discovery has been proven a useful and cost-effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin-dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand-kinase inhibition constants (pKi ) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph-based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross-validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand-kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.

cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules

The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.

toxCSM: comprehensive prediction of small molecule toxicity profiles

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Polymer-drug conjugates: Design principles, emerging synthetic strategies and clinical overview

Adagen, an enzyme replacement treatment for adenosine deaminase deficiency, was the first protein-polymer conjugate to be approved in early 1990s. Post this regulatory approval, numerous polymeric drugs and polymeric nanoparticles have entered the market as advanced or next-generation polymer-based therapeutics, while many others have currently been tested clinically. The polymer conjugation to therapeutic moiety offers several advantages, like enhanced solubilization of drug, controlled release, reduced immunogenicity, and prolonged circulation. The present review intends to highlight considerations in the design of therapeutically effective polymer-drug conjugates (PDCs), including the choice of linker chemistry. The potential synthetic strategies to formulate PDCs have been discussed along with recent advancements in the different types of PDCs, i.e., polymer-small molecular weight drug conjugates, polymer-protein conjugates, and stimuli-responsive PDCs, which are under clinical/preclinical investigation. Current impediments and regulatory hurdles hindering the clinical translation of PDC into effective therapeutic regimens for the amelioration of disease conditions have been addressed.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Investigation of the Associations between a Nanomaterial's Microrheology and Toxicology

With the advent of Nanotechnology, the use of nanomaterials in consumer products is increasing on a daily basis, due to which a deep understanding and proper investigation regarding their safety and risk assessment should be a major priority. To date, there is no investigation regarding the microrheological properties of nanomaterials (NMs) in biological media. In our study, we utilized in silico models to select the suitable NMs based on their physicochemical properties such as solubility and lipophilicity. Then, we established a new method based on dynamic light scattering (DLS) microrheology to get the mean square displacement (MSD) and viscoelastic property of two model NMs that are dendrimers and cerium dioxide nanoparticles in Dulbecco's Modified Eagle Medium (DMEM) complete media at three different concentrations for both NMs. Subsequently, we established the cytotoxicological profiling using water-soluble tetrazolium salt-1 (WST-1) and a reactive oxygen species (ROS) assay. To take one step forward, we further looked into the tight junction properties of the cells using immunostaining with Zonula occluden-1 (ZO-1) antibodies and found that the tight junction function or transepithelial resistance (TEER) was affected in response to the microrheology and cytotoxicity. The quantitative polymerase chain reaction (q-PCR) results in the gene expression of ZO-1 after the 24 h treatment with NPs further validates the findings of immunostaining results. This new method that we established will be a reference point for other NM studies which are used in our day-to-day consumer products.

cropCSM: designing safe and potent herbicides with graph-based signatures

Herbicides have revolutionised weed management, increased crop yields and improved profitability allowing for an increase in worldwide food security. Their widespread use, however, has also led to a rise in resistance and concerns about their environmental impact. Despite the need for potent and safe herbicidal molecules, no herbicide with a new mode of action has reached the market in 30 years. Although development of computational approaches has proven invaluable to guide rational drug discovery pipelines, leading to higher hit rates and lower attrition due to poor toxicity, little has been done in contrast for herbicide design. To fill this gap, we have developed cropCSM, a computational platform to help identify new, potent, nontoxic and environmentally safe herbicides. By using a knowledge-based approach, we identified physicochemical properties and substructures enriched in safe herbicides. By representing the small molecules as a graph, we leveraged these insights to guide the development of predictive models trained and tested on the largest collected data set of molecules with experimentally characterised herbicidal profiles to date (over 4500 compounds). In addition, we developed six new environmental and human toxicity predictors, spanning five different species to assist in molecule prioritisation. cropCSM was able to correctly identify 97% of herbicides currently available commercially, while predicting toxicity profiles with accuracies of up to 92%. We believe cropCSM will be an essential tool for the enrichment of screening libraries and to guide the development of potent and safe herbicides. We have made the method freely available through a user-friendly webserver at http://biosig.unimelb.edu.au/crop_csm.

DiZyme: Open-Access Expandable Resource for Quantitative Prediction of Nanozyme Catalytic Activity

Enzymes suffer from high cost, complex purification, and low stability. Development of low-cost artificial enzymes of comparative or higher effectiveness is desired. Given its complexity, it is desired to presume their activities prior to experiments. While computational approaches demonstrate success in modeling nanozyme activities, they require assumptions about the system to be made. Machine learning (ML) is an alternative approach towards data-driven material property prediction achieving high performance even on multicomponent complex systems. Despite the growing demand for customized nanozymes, there is no open access nanozyme database. Here, a user-friendly expandable database of >300 existing inorganic nanozymes is developed by data collection from >100 articles. Data analysis is performed to reveal the features responsible for catalytic activities of nanozymes, and new descriptors are proposed for its ML-assisted prediction. A random forest regression (RFR) model for evaluation of nanozyme peroxidase activity is developed and optimized by correlation-based feature selection and hyperparameter tuning, achieving performance up to R²  = 0.796 for K_cat and R²  = 0.627 for K_m . Experiment-confirmed unknown nanozyme activity prediction is also demonstrated. Moreover, the DiZyme expandable, open-access resource containing the database, predictive algorithm, and visualization tool is developed to boost novel nanozyme discovery worldwide (https://dizyme.net).

pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.

pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures

MOTIVATION

G protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands.

RESULTS

Bioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation.

AVAILABILITY AND IMPLEMENTATION

pdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at http://biosig.unimelb.edu.au/pdcsm_gpcr/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Poly(HPMA-co-NIPAM) copolymer as an alternative to polyethylene glycol-based pharmacokinetic modulation of therapeutic proteins

PEGylation is the standard approach for prolonging the plasma exposure of protein therapeutics but has limitations. We explored whether polymers prepared by Reversible Addition-Fragmentation chain-Transfer (RAFT) may provide better alternatives to polyethylene glycol (PEG). Four RAFT polymers were synthesised with varying compositions, molar mass (M_n), and structures, including a homopolymer of N-(2-hydroxypropyl)methacrylamide, (pHPMA) and statistical copolymers of HPMA with poly(ethylene glycol methyl ether acrylate) p(HPMA-co-PEGA); HPMA and N-acryloylmorpholine, p(HPMA-co-NAM); and HPMA and N-isopropylacrylamide, p(HPMA-co-NIPAM). The intravenous pharmacokinetics of the polymers were then evaluated in rats. The in vitro activity and in vivo pharmacokinetics of p(HPMA-co-NIPAM)-conjugated trastuzumab Fab' and full length mAb were then evaluated. p(HPMA-co-NIPAM) prolonged plasma exposure more avidly compared to the other p(HPMA) polymers or PEG, irrespective of molecular weight. When conjugated to trastuzumab-Fab', p(HPMA-co-NIPAM) prolonged plasma exposure of the Fab' similar to PEG-Fab'. The generation of anti-PEG IgM in rats 7 days after intravenous and subcutaneous dosing of p(HPMA-co-NIPAM) conjugated trastuzumab mAb was also examined and was shown to exhibit lower immunogenicity than the PEGylated construct. These data suggest that p(HPMA-co-NIPAM) has potential as a promising copolymer for use as an alternative conjugation strategy to PEG, to prolong the plasma exposure of therapeutic proteins.

epitope3D: a machine learning method for conformational B-cell epitope prediction

The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been developed over the past two decades to assist in epitope prediction; however, they have presented limited performance and generalization, particularly for the identification of conformational B-cell epitopes. Here, we present epitope3D, a novel scalable machine learning method capable of accurately identifying conformational epitopes trained and evaluated on the largest curated epitope data set to date. Our method uses the concept of graph-based signatures to model epitope and non-epitope regions as graphs and extract distance patterns that are used as evidence to train and test predictive models. We show epitope3D outperforms available alternative approaches, achieving Mathew's Correlation Coefficient and F1-scores of 0.55 and 0.57 on cross-validation and 0.45 and 0.36 during independent blind tests, respectively.

Nanomaterial-based drug delivery systems as promising carriers for patients with COVID-19

Once the World Health Organization (WHO) declared the COVID-19 outbreak to be pandemic, massive efforts have been launched by researchers around the globe to combat this emerging infectious disease. Here we review the most recent data on the novel SARS-CoV-2 pathogen. We analyzed its etiology, pathogenesis, diagnosis, prevention, and current medications. After that, we summarized the promising drug delivery application of nanomaterial-based systems. Their preparation routes, unique advantages over the traditional drug delivery routes and their toxicity though risk analysis were also covered. We also discussed in detail the mechanism of action for one example of drug-loaded nanomaterial drug delivery systems (Avigan-contained nano-emulsions). This review provides insights about employing nanomaterial-based drug delivery systems for the treatment of COVID-19 to increase the bioavailability of current drugs, reducing their toxicity, and to increase their efficiency.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions

Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure–activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson’s correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at http://biosig.unimelb.edu.au/pdcsm_cancer.

The Impact of Polymer Size and Cleavability on the Intravenous Pharmacokinetics of PEG-Based Hyperbranched Polymers in Rats

A better understanding of the impact of molecular size and linkers is important for PEG-based hyperbranched polymers (HBPs) intended as tailored drug delivery vehicles. This study aimed to evaluate the effects of crosslinker chemistry (cleavable disulphide versus non-cleavable ethylene glycol methacrylate (EGDMA) linkers) and molecular weight within the expected size range for efficient renal elimination (22 vs. 48 kDa) on the intravenous pharmacokinetic and biodistribution properties of 89Zr-labelled HBPs in rats. All HBPs showed similar plasma pharmacokinetics over 72 h, despite differences in linker chemistry and size. A larger proportion of HBP with the cleavable linker was eliminated via the urine and faeces compared to a similar-sized HBP with the non-cleavable linker, while size had no impact on the proportion of the dose excreted. The higher molecular weight HBPs accumulated in organs of the mononuclear phagocyte system (liver and spleen) more avidly than the smaller HBP. These results suggest that HBPs within the 22 to 48 kDa size range show no differences in plasma pharmacokinetics, but distinct patterns of organ biodistribution and elimination are evident.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria

Development of new potent, safe drugs to treat Mycobacteria has proven to be challenging, with limited hit rates of initial screens restricting subsequent development efforts. Despite significant efforts and the evolution of quantitative structure-activity relationship as well as machine learning-based models for computationally predicting molecule bioactivity, there is an unmet need for efficient and reliable methods for identifying biologically active compounds against Mycobacterium that are also safe for humans. Here we developed mycoCSM, a graph-based signature approach to rapidly identify compounds likely to be active against bacteria from the genus Mycobacterium, or against specific Mycobacteria species. mycoCSM was trained and validated on eight organism-specific and for the first time a general Mycobacteria data set, achieving correlation coefficients of up to 0.89 on cross-validation and 0.88 on independent blind tests, when predicting bioactivity in terms of minimum inhibitory concentration. In addition, we also developed a predictor to identify those compounds likely to penetrate in necrotic tuberculosis foci, which achieved a correlation coefficient of 0.75. Together with a built-in estimator of the maximum tolerated dose in humans, we believe this method will provide a valuable resource to enrich screening libraries with potent, safe molecules. To provide simple guidance in the selection of libraries with favorable anti-Mycobacteria properties, we made mycoCSM freely available online at http://biosig.unimelb.edu.au/myco_csm.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

An update on the use of molecular modeling in dendrimers design for biomedical applications: are we using its full potential?

INTRODUCTION

Dendrimers are well-defined hyperbranched polymers built from a variety of different monomers and with tuneable properties that make them suitable for different biomedical applications. Their three-dimensional (3D) structure cannot be usually determined experimentally due to their inherent nature of repeating patterns in the topology, failure to crystalize, and/or high flexibility. Therefore, their conformations and interactions at the atomistic level can be studied only by using computational chemistry methods, including molecular dynamics, Monte Carlo simulations, and molecular docking.

AREAS COVERED

In this review, the methods that could be utilized in computer-aided dendrimer design are considered, providing a list of approaches to generate initial 3D coordinates and selected examples of applications of relevant molecular modeling methods.

EXPERT OPINION

Computational chemistry provides an invaluable set of tools to study dendrimers and their interactions with drugs and biological targets. There is a gap in the software development that is dedicated to study of these highly variable and complex systems that could be overcome by the integration of already established approaches for topology generation and open source molecular modeling libraries. Furthermore, it would be highly beneficial to collate already built 3D models of various dendrimers with corresponding relevant experimental data.

The Role of Branch Cell Symmetry and Other Critical Nanoscale Design Parameters in the Determination of Dendrimer Encapsulation Properties

This article reviews progress over the past three decades related to the role of dendrimer-based, branch cell symmetry in the development of advanced drug delivery systems, aqueous based compatibilizers/solubilizers/excipients and nano-metal cluster catalysts. Historically, it begins with early unreported work by the Tomalia Group (i.e., The Dow Chemical Co.) revealing that all known dendrimer family types may be divided into two major symmetry categories; namely: Category I: symmetrical branch cell dendrimers (e.g., Tomalia, Vögtle, Newkome-type dendrimers) possessing interior hollowness/porosity and Category II: asymmetrical branch cell dendrimers (e.g., Denkewalter-type) possessing no interior void space. These two branch cell symmetry features were shown to be pivotal in directing internal packing modes; thereby, differentiating key dendrimer properties such as densities, refractive indices and interior porosities. Furthermore, this discovery provided an explanation for unimolecular micelle encapsulation (UME) behavior observed exclusively for Category I, but not for Category II. This account surveys early experiments confirming the inextricable influence of dendrimer branch cell symmetry on interior packing properties, first examples of Category (I) based UME behavior, nuclear magnetic resonance (NMR) protocols for systematic encapsulation characterization, application of these principles to the solubilization of active approved drugs, engineering dendrimer critical nanoscale design parameters (CNDPs) for optimized properties and concluding with high optimism for the anticipated role of dendrimer-based solubilization principles in emerging new life science, drug delivery and nanomedical applications.