Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Targeted Affinity Purification and Mechanism of Action of Angiotensin-Converting Enzyme (ACE) Inhibitory Peptides from Sea Cucumber Gonads

Protein hydrolysates from sea cucumber (Apostichopus japonicus) gonads are rich in active materials with remarkable angiotensin-converting enzyme (ACE) inhibitory activity. Alcalase was used to hydrolyze sea cucumber gonads, and the hydrolysate was separated by the ultrafiltration membrane to produce a low-molecular-weight peptide component (less than 3 kDa) with good ACE inhibitory activity. The peptide component (less than 3 kDa) was isolated and purified using a combination method of ACE gel affinity chromatography and reverse high-performance liquid chromatography. The purified fractions were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the resulting products were filtered using structure-based virtual screening (SBVS) to obtain 20 peptides. Of those, three noncompetitive inhibitory peptides (DDQIHIF with an IC50 value of 333.5 μmol·L-1, HDWWKER with an IC50 value of 583.6 μmol·L-1, and THDWWKER with an IC50 value of 1291.8 μmol·L-1) were further investigated based on their favorable pharmacochemical properties and ACE inhibitory activity. Molecular docking studies indicated that the three peptides were entirely enclosed within the ACE protein cavity, improving the overall stability of the complex through interaction forces with the ACE active site. The total free binding energies (ΔGtotal) for DDQIHIF, HDWWKER, and THDWWKER were -21.9 Kcal·mol-1, -71.6 Kcal·mol-1, and -69.1 Kcal·mol-1, respectively. Furthermore, a short-term assay of antihypertensive activity in spontaneously hypertensive rats (SHRs) revealed that HDWWKER could significantly decrease the systolic blood pressure (SBP) of SHRs after intravenous administration. The results showed that based on the better antihypertensive activity of the peptide in SHRs, the feasibility of targeted affinity purification and computer-aided drug discovery (CADD) for the efficient screening and preparation of ACE inhibitory peptide was verified, which provided a new idea of modern drug development method for clinical use.

Big data and artificial intelligence in cancer research

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.

Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design)

In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.

The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.

Scaffold repositioning of spiro-acridine derivatives as fungi chitinase inhibitor by target fishing and in vitro studies

The concept of "one target, one drug, one disease" is not always true, as compounds with previously described therapeutic applications can be useful to treat other maladies. For example, acridine derivatives have several potential therapeutic applications. In this way, identifying new potential targets for available drugs is crucial for the rational management of diseases. Computational methodologies are interesting tools in this field, as they use rational and direct methods. Thus, this study focused on identifying other rational targets for acridine derivatives by employing inverse virtual screening (IVS). This analysis revealed that chitinase enzymes can be potential targets for these compounds. Subsequently, we coupled molecular docking consensus analysis to screen the best chitinase inhibitor among acridine derivatives. We observed that 3 compounds displayed potential enhanced activity as fungal chitinase inhibitors, showing that compound 5 is the most active molecule, with an IC₅₀ of 0.6 ng/µL. In addition, this compound demonstrated a good interaction with the active site of chitinases from Aspergillus fumigatus and Trichoderma harzianum. Additionally, molecular dynamics and free energy demonstrated complex stability for compound 5. Therefore, this study recommends IVS as a powerful tool for drug development. The potential applications are highlighted as this is the first report of spiro-acridine derivatives acting as chitinase inhibitors that can be potentially used as antifungal and antibacterial candidates.

TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa

Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

Can We Quickly Learn to "Translate" Bioactive Molecules with Transformer Models?

Meaningful exploration of the chemical space of druglike molecules in drug design is a highly challenging task due to a combinatorial explosion of possible modifications of molecules. In this work, we address this problem with transformer models, a type of machine learning (ML) model originally developed for machine translation. By training transformer models on pairs of similar bioactive molecules from the public ChEMBL data set, we enable them to learn medicinal-chemistry-meaningful, context-dependent transformations of molecules, including those absent from the training set. By retrospective analysis on the performance of transformer models on ChEMBL subsets of ligands binding to COX2, DRD2, or HERG protein targets, we demonstrate that the models can generate structures identical or highly similar to most active ligands, despite the models having not seen any ligands active against the corresponding protein target during training. Our work demonstrates that human experts working on hit expansion in drug design can easily and quickly employ transformer models, originally developed to translate texts from one natural language to another, to "translate" from known molecules active against a given protein target to novel molecules active against the same target.

Insights into the Effects and Mechanism of Andrographolide-Mediated Recovery of Susceptibility of Methicillin-Resistant Staphylococcus aureus to β-Lactam Antibiotics

The frequent resistance associated with β-lactam antibiotics and the high frequency of mutations in β-lactamases constitute a major clinical challenge that can no longer be ignored. Andrographolide (AP), a natural active compound, has been shown to restore susceptibility to β-lactam antibiotics. Fluorescence quenching and molecular simulation showed that AP quenched the intrinsic fluorescence of β-lactamase BlaZ and stably bound to the residues in the catalytic cavity of BlaZ. Of note, AP was found to reduce the stability of the cell wall (CW) in methicillin-resistant Staphylococcus aureus (MRSA), and in combination with penicillin G (PEN), it significantly induced CW roughness and dispersion and even caused its disintegration, while the same concentration of PEN did not. In addition, transcriptome sequencing revealed that AP induced a significant stress response and increased peptidoglycan (PG) synthesis but disrupted its cross-linking, and it repressed the expression of critical genes such as mecA, blaZ, and sarA. We also validated these findings by quantitative reverse transcription-PCR (qRT-PCR). Association analysis using the GEO database showed that the alterations caused by AP were similar to those caused by mutations in the sarA gene. In summary, AP was able to restore the susceptibility of MRSA to β-lactam antibiotics, mainly by inhibiting the β-lactamase BlaZ, by downregulating the expression of critical resistance genes such as mecA and blaZ, and by disrupting CW homeostasis. In addition, restoration of susceptibility to antibiotics could be achieved by inhibiting the global regulator SarA, providing an effective solution to alleviate the problem of bacterial resistance. IMPORTANCE Increasingly, alternatives to antibiotics are being used to mitigate the rapid onset and development of bacterial resistance, and the combination of natural compounds with traditional antibiotics has become an effective therapeutic strategy. Therefore, we attempted to discover more mechanisms to restore susceptibility and effective dosing strategies. Andrographolide (AP), as a natural active ingredient, can mediate recovery of susceptibility of MRSA to β-lactam antibiotics. AP bound stably to the β-lactamase BlaZ and impaired its hydrolytic activity. Notably, AP was able to downregulate the expression of critical resistance genes such as mecA, blaZ, and sarA. Meanwhile, it disrupted the CW cross-linking and homeostasis, while the same concentration of penicillin could not. The multiple inhibitory effect of AP resensitizes intrinsically resistant bacteria to β-lactam antibiotics, effectively prolonging the use cycle of these antibiotics and providing an effective solution to reduce the dosage of antibiotics and providing a theoretical reference for the prevention and control of MRSA.

A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study

A new lead compound has been designed as an antiangiogenic EGFR inhibitor that has the pharmacophoric characteristics to bind with the catalytic pocket of EGFR protein. The designed lead compound is a (para-chloro)acetamide derivative of the alkaloid, theobromine, (T-1-PCPA). At first, we started with deep density functional theory (DFT) calculations for T-1-PCPA to confirm and optimize its 3D structure. Additionally, the DFT studies identified the electrostatic potential, global reactive indices and total density of states expecting a high level of reactivity for T-1-PCPA. Secondly, the affinity of T-1-PCPA to bind and inhibit the EGFR protein was studied and confirmed through detailed structure-based computational studies including the molecular docking against EGFR^WT and EGFR^T790M, Molecular dynamics (MD) over 100 ns, MM-GPSA and PLIP experiments. Before the preparation, the computational ADME and toxicity profiles of T-1-PCPA have been investigated and its safety and the general drug-likeness predicted. Accordingly, T-1-PCPA was semi-synthesized to scrutinize the proposed design and the obtained in silico results. Interestingly, T-1-PCPA inhibited in vitro EGFR^WT with an IC₅₀ value of 25.35 nM, comparing that of erlotinib (5.90 nM). Additionally, T-1-PCPA inhibited the growth of A549 and HCT-116 malignant cell lines with IC₅₀ values of 31.74 and 20.40 µM, respectively, comparing erlotinib that expressed IC₅₀ values of 6.73 and 16.35 µM, respectively.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Polo-like Kinase 1 Inhibitors in Human Cancer Therapy: Development and Therapeutic Potential

Polo-like kinase 1 (PLK1) plays an important role in a variety of cellular functions, including the regulation of mitosis, DNA replication, autophagy, and the epithelial-mesenchymal transition (EMT). PLK1 overexpression is often associated with cell proliferation and poor prognosis in cancer patients, making it a promising antitumor target. To date, at least 10 PLK1 inhibitors (PLK1i) have been entered into clinical trials, among which the typical kinase domain (KD) inhibitor BI 6727 (volasertib) was granted "breakthrough therapy designation" by the FDA in 2013. Unfortunately, many other KD inhibitors showed poor specificity, resulting in dose-limiting toxicity, which has greatly impeded their development. Researchers recently discovered many PLK1i with higher selectivity, stronger potency, and better absorption, distribution, metabolism, and elimination (ADME) characteristics. In this review, we emphasize the structure-activity relationships (SARs) of PLK1i, providing insights into new drugs targeting PLK1 for antitumor clinical practice.

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.