The application of machine learning techniques to innovative antibacterial discovery and development

Applications of model simulation in pharmacological fields and the problems of theoretical reliability

The use of mathematical models has become increasingly prevalent in pharmacological fields, particularly in drug development processes. These models are instrumental in tasks such as designing clinical trials and assessing factors like efficacy, toxicity, and clinical practice. Various types of models have been developed and documented. Nevertheless, emphasizing the reliability of parameter values is crucial, as they play a pivotal role in shaping the behavior of the system. In some instances, parameter values reported previously are treated as fixed values, which can lead to convergence towards values that deviate substantially from those found in actual biological systems. This is especially true when parameter values are determined through fitting to limited observations. To mitigate this risk, the reuse of parameter values from previous reports should be approached with a critical evaluation of their validity. Currently, there is a proposal for a simultaneous search for plausible values for all parameters using comprehensive search algorithms in both pharmacokinetic and pharmacodynamic or systems pharmacological models. Implementing these methodologies can help address issues related to parameter determination. Furthermore, integrating these approaches with methods developed in the field of machine-learning field has the potential to enhance the reliability of parameter values and the resulting model outputs.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.

Identification of potential extracellular signal-regulated protein kinase 2 inhibitors based on multiple virtual screening strategies

The integration of multiple virtual screening strategies facilitates the balance of computational efficiency and prediction accuracy. In this study, we constructed an efficient and reliable "multi-stage virtual screening-in vitro biological validation" system to identify potential inhibitors targeting extracellular signal-regulated protein kinase 2 (ERK2). Firstly, we rapidly obtained 10 candidate ERK2 inhibitors with desirable pharmacokinetic characteristics from thousands of named natural products in ZINC database based on machine learning classification models and ADME/T prediction. The structure-based molecular docking approach was then used to obtain four further hits with lower binding free energy compared to the positive control molecule Magnolipin. Subsequently, the two compounds were purchased for in vitro biological validation considering commercial availability and economic cost, and the results showed that Dodoviscin A exhibited acceptable inhibitory activity on ERK2 (IC50 = 10.79 μm). Finally, the mechanism of action and binding stability of this natural product inhibitor were investigated by binding mode analysis and molecular dynamics simulation.

Multi-stage virtual screening of natural products against p38α mitogen-activated protein kinase: predictive modeling by machine learning, docking study and molecular dynamics simulation

p38α is a mitogen-activated protein kinase (MAPK), and the signaling pathways involved are closely related to the inflammation, apoptosis and differentiation of cells, which also makes it an attractive target for drug discovery. With the high efficiency and low cost, virtual screening technology is becoming an indispensable part of drug development. In this study, a novel multi-stage virtual screening method based on machine learning, molecular docking and molecular dynamics simulation was developed to identify p38α MAPK inhibitors from natural products in ZINC database, which improves the prediction accuracy by considering and utilizing both ligand and receptor information compared to any individual approach. Ultimately, we screened out two candidate inhibitors with acceptable ADMET properties (ZINC4260400 and ZINC8300300). Among the generated machine learning models, Random Forest (RF) and Support Vector Machine (SVM) performed better, with the area under the receiver operating characteristic curve (AUC) values of 0.932 and 0.931 on the test set, as well as 0.834 and 0.850 on the external validation set. In addition, the results of molecular docking and ADMET prediction showed that two compounds with appropriate pharmacokinetic properties had binding free energies less than −8.0 kcal/mol for the target protein, and the results of molecular dynamics simulations further confirmed that they were stable during the process of inhibition.

Fluoroquinolones Hybrid Molecules as Promising Antibacterial Agents in the Fight against Antibacterial Resistance

The emergence of bacterial resistance has motivated researchers to discover new antibacterial agents. Nowadays, fluoroquinolones keep their status as one of the essential classes of antibacterial agents. The new generations of fluoroquinolones are valuable therapeutic tools with a spectrum of activity, including Gram-positive, Gram-negative, and atypical bacteria. This review article surveys the design of fluoroquinolone hybrids with other antibacterial agents or active compounds and underlines the new hybrids' antibacterial properties. Antibiotic fluoroquinolone hybrids have several advantages over combined antibiotic therapy. Thus, some challenges related to joining two different molecules are under study. Structurally, the obtained hybrids may contain a cleavable or non-cleavable linker, an essential element for their pharmacokinetic properties and mechanism of action. The design of hybrids seems to provide promising antibacterial agents helpful in the fight against more virulent and resistant strains. These hybrid structures have proven superior antibacterial activity and less susceptibility to bacterial resistance than the component molecules. In addition, fluoroquinolone hybrids have demonstrated other biological effects such as anti-HIV, antifungal, antiplasmodic/antimalarial, and antitumor activity. Many fluoroquinolone hybrids are in various phases of clinical trials, raising hopes that new antibacterial agents will be approved shortly.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Direct Lytic Agents: Novel, Rapidly Acting Potential Antimicrobial Treatment Modalities for Systemic Use in the Era of Rising Antibiotic Resistance

Direct lytic agents (DLAs) are novel antimicrobial compounds with unique mechanisms of action based on rapid cell wall destabilization and bacteriolysis. DLAs include two classes of purified polypeptides—lysins (peptidoglycan hydrolase enzymes) and amurins (outer membrane targeting peptides). Their intended use is to kill bacteria in a manner that is complimentary to and synergistic with traditional antibiotics without selection for DLA resistance. Lysins were originally described as having activity against Gram-positive pathogens and of those, exebacase, is the first to have advanced into Phase 3 of clinical development. Recently, both engineered and native DLAs have now been described with potent bactericidal activity against a range of Gram-negative pathogens, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii. Importantly, novel DLAs targeting Gram-negatives, including the lysin CF-370 and the amurin peptides, are active in biological matrices (blood/serum) and, as such, offer promise for therapeutic use as systemically administered agents for the treatment of life-threatening invasive infections. In this review, DLAs are discussed as potential new classes of antimicrobial biologics that can be used to treat serious systemic infections.

Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated

Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC₅₀), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems

Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL^-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Success stories of AI in drug discovery - where do things stand?

INTRODUCTION

Artificial intelligence (AI) in drug discovery and development (DDD) has gained more traction in the past few years. Many scientific reviews have already been made available in this area. Thus, in this review, the authors have focused on the success stories of AI-driven drug candidates and the scientometric analysis of the literature in this field.

AREA COVERED

The authors explore the literature to compile the success stories of AI-driven drug candidates that are currently being assessed in clinical trials or have investigational new drug (IND) status. The authors also provide the reader with their expert perspectives for future developments and their opinions on the field.

EXPERT OPINION

Partnerships between AI companies and the pharma industry are booming. The early signs of the impact of AI on DDD are encouraging, and the pharma industry is hoping for breakthroughs. AI can be a promising technology to unveil the greatest successes, but it has yet to be proven as AI is still at the embryonic stage.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

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.

First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery

Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure-activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors.

A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles

The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model's validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.