Search Results (10)

Background/Objectives: Severe malaria, mainly caused by Plasmodium falciparum, remains a significant therapeutic challenge due to increasing drug resistance and adverse effects. Flavonoids, known for their wide range of bioactivities, offer a promising route for antimalarial drug discovery. The aim of this study was to elucidate key structural features associated with antimalarial activity in flavonoids and to develop accurate, interpretable predictive models. Methods: Curated databases of flavonoid structures and their activity against P. falciparum strains and enzymes were constructed. Molecular fingerprinting and decision tree analyses were used to identify key pharmacophoric groups. Subsequently, molecular descriptors were generated and reduced to build multiple classification and regression models. Results: These models demonstrated high predictive accuracy, with test set accuracies ranging from 92.85% to 100%, and R² values from 0.64 to 0.97. Virtual screening identified novel flavonoid candidates with potential inhibitory activity. These were further evaluated using molecular docking and molecular dynamics simulations to assess binding affinity and stability with Plasmodium proteins (FabG, FabZ, and FabI). The predicted active ligands exhibited stable pharmacophore interactions with key protein residues, providing insights into binding mechanisms. Conclusions: This study provides highly predictive models for antimalarial flavonoids and enhances the understanding of structure–activity relationships, offering a strong foundation for further experimental validation. Full article

Background: Malaria remains a significant global health burden, particularly in sub-Saharan Africa, accounting for high rates of illness and death. The growing resistance to frontline antimalarial therapies underscores the urgent need for novel drug targets and therapeutics. Bromodomain-containing proteins, which regulate gene expression through chromatin remodeling, have gained attention as potential targets. Plasmodium falciparum bromodomain protein 1 (PfBDP1), a 55 kDa nuclear protein, plays a key role in recognizing acetylated lysine residues and facilitating transcription during parasite development. Methods: This study investigated ex vivo PfBDP1 gene mutations and identified potential small molecule inhibitors using computational approaches. Malaria-positive blood samples were collected. Genomic DNA was extracted, assessed for quality, and amplified using PfBDP1-specific primers. DNA sequencing and alignment were performed to determine single-nucleotide polymorphism (SNP). Structural modeling used the PfBDP1 crystal structure (PDB ID: 7M97), and active site identification was conducted using CASTp 3.0. Virtual screening and pharmacophore modeling were performed using Pharmit and AutoDock Vina, followed by ADME/toxicity evaluations with SwissADME, OSIRIS, and Discovery Studio. GROMACS was used for 100 ns molecular dynamics simulations. Results: The malaria prevalence rate stood at 12.24%, and the sample size was 165. Sequencing results revealed conserved PfBDP1 gene sequences compared to the 3D7 reference strain. Virtual screening identified nine lead compounds with binding affinities ranging from −9.8 to −10.7 kcal/mol. Of these, CHEMBL2216838 had a binding affinity of −9.9 kcal/mol, with post-screening predictions of favorable drug-likeness (8.60), a high drug score (0.78), superior pharmacokinetics, and a low toxicity profile compared to chloroquine. Molecular dynamics simulations confirmed its stable interaction within the PfBDP1 active site. Conclusions: Overall, this study makes a significant contribution to the ongoing search for novel antimalarial drug targets by providing both molecular and computational evidence for PfBDP1 as a promising therapeutic target. The prediction of CHEMBL2216838 as a lead compound with favorable binding affinity, drug-likeness, and safety profile, surpassing those of existing drugs like chloroquine, sets the stage for preclinical validation and further structure-based drug design efforts. These findings are supported by prior experimental evidence showing significant parasite inhibition and gene suppression capability of predicted hits. Full article

The methods utilized in the drug discovery pipeline routinely combine machine learning and deep learning algorithms to enhance the outputs. The generation of a drug target, through virtual screening and computational analysis of databases used for target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. Recent technological advances in human immunology have provided improved tools that allow a better understanding of the biological and molecular mechanisms leading to the protective human immune response to pathogens, inspiring new strategies for vaccine design. Immunoinformatics approaches are more beneficial, and thus there is a demand for modern technologies such as reverse vaccinology, structural vaccinology, and system approaches in developing potential vaccine candidates. System theory, defined as a set of machine learning, control theory, and optimization-based methods applied to networked systems, provides a unifying framework for modeling and analyzing biological complexity. In this review, we explore the application of such computational methods at every stage of the therapeutic pipeline, including lead discovery, optimization, and dosing, as well as vaccine target prediction and immunogen design. Here, we summarize the system theoretic methods which provide insights into developed approaches and their applications in rational drug discovery and vaccine formulations. The approaches ranged in the review yield accurate predictions and insights. This review is intended to serve as a resource for researchers seeking to understand, adopt, or build upon system theoretic techniques in drug and vaccine development, offering both conceptual foundations and practical directions. Full article

Background: One of the challenges of applying artificial intelligence (AI) methods to drug discovery is the difficulty of laboratory synthesizability for many AI-discovered molecules. Often, in silico techniques and metrics such as the computationally enabled synthesizability score and AI-based retrosynthesis analysis are used. Methods: In this paper, we present a predictive synthesizability method that integrates the gains of synthetic accessibility scoring and the benefits of AI-driven retrosynthesis analysis tools to evaluate the synthesizability of AI-generated lead drug molecules. Results: We explored the proposed method by using it to analyze the synthesizability of a set of 123 novel molecules generated using AI models. The analysis of the synthesis route of the four best molecules from the set in terms of synthesizability, as identified using the proposed method, is presented. Conclusions: This strategy enables quick initial screening and more comprehensive actionable synthetic pathways, thereby balancing speed and detail, and favoring simple routes to avoid the risk of pursuing non-synthesizable compounds in the drug development pipeline. Full article

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, disabling disorder of unknown etiology, poor prognosis, and limited therapeutic options. Previously, 3′5-dimaleamylbenzoic acid (3′5-DMBA) was shown to exert resolving effects in IPF, offering a promising alternative for treating this disease; however, the molecular mechanisms associated with this effect have not been explored. Objetive: We evaluated the potential antifibrotic mechanisms of 3′5-DMBA by network pharmacology (NP) and molecular docking (MD). Methods: 3′5-DMBA-associated targets were identified by screening in SwissTargetPrediction. IPF-associated targets were identified using lung tissue meta-analysis and public databases. Common targets were identified, and a protein–protein interaction (PPI) network was constructed; we ranked the proteins in the PPI network by topological analysis. MD validated the binding of 3′5-DMBA to the main therapeutic targets. Results: A total of 57 common targets were identified between 3′5-DMBA and IPF; caspase 8, 9, 3, and 7; myeloid leukemia-induced cell differentiation protein Mcl-1; and poly [ADP-ribose] polymerase 1 are primary targets regulating PPI networks. Functional analysis revealed that the common targets are involved in the pathological features of tissue fibrosis and primarily in the apoptotic process. MD revealed favorable interaction energies among the three main targets regulating PPI networks. Conclusions: NP results suggest that the antifibrotic effect of 3′5-DMBA is due to its regulation of the pathological features of IPF, mainly by modulating signaling pathways leading to apoptosis, suggesting its therapeutic potential to treat this disease. Full article

Background: Staphylococcus aureus is a pathogen that has become resistant to different antibiotics, which makes it a threat to human health. Although the first penicillin-resistant strain appeared in 1945, nowadays, there are just a few alternatives to fight it. To circumvent this issue, novel approaches to develop drugs to target proteins of the bacteria cytoskeleton, essential for bacteria’s binary fission, are being developed. FtsZ and FtsA are two proteins that are key for the initial stages of binary fission. On one side, FtsZ forms a polymeric circular structure called the Z ring; meanwhile, FtsA binds to the cell membrane and then anchors to the Z ring. According to the literature, this interaction occurs within the C-terminus domain of FtsZ, which is mainly disordered. Objective: In this work, we studied the binding of FtsZ to FtsA using computational chemistry tools to identify the interactions between the two proteins to further use this information for the search of potential protein-protein binding inhibitors (PPBIs). Methods: We made a bioinformatic analysis to obtain a representative sequence of FtsZ and FtsA of Staphylococcus aureus. With this information, we built homology models of the FtsZ to carry out the molecular docking with the FtsA. Furthermore, alanine scanning was conducted to identify the key residues forming the FtsZ–FtsA complex. Finally, we used this information to generate a pharmacophore model to carry out a virtual screening approach. Results: We identified the key residues forming the FtsZ-FtsA complex as well as five molecules with high potential as PPBIs. Full article

Fragment libraries have a major significance in drug discovery due to their role in de novo design and enumerating large and ultra-large compound libraries. Although several fragment libraries are commercially available, most are derived from synthetic compounds. The number of fragment libraries derived from natural products is still being determined. Still, they represent a rich source of building blocks to generate pseudo-natural products and bioactive synthetic compounds inspired by natural products. In this work, we generated and analyzed a fragment library of natural products from Colombia, a highly diverse geographical region where fragment libraries are yet to be reported. We also generated and reported fragment libraries of three novel natural product libraries and, as a reference, the most updated version of FDA-approved drugs. In line with the principles of open science, the fragment libraries developed in this study are freely available. Full article

The drug discovery and development process is very lengthy, highly expensive, and extremely complex in nature. Considering the time and cost constraints associated with conventional drug discovery, new methods must be found to enhance the declining efficiency of traditional approaches. Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Advancements in AI and machine learning (ML) techniques have revolutionized their applications to drug discovery and development. This review illuminates the profound influence of AI on diverse aspects of drug discovery, encompassing drug-target identification, molecular properties, compound analysis, drug development, quality assurance, and drug toxicity assessment. ML algorithms play an important role in testing systems and can predict important aspects such as the pharmacokinetics and toxicity of drug candidates. This review not only strengthens the theoretical foundation and development of this technology, but also explores the myriad challenges and promising prospects of AI in drug discovery and development. The combination of AI and drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry. Full article

Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process. Full article

Therapeutic phages against pathogenic bacteria should kill the bacteria efficiently before the latter evolve resistance against the phages. While many factors contribute to phage efficiency in killing bacteria, such as phage attachment to host, delivery of phage genome into the host, phage mechanisms against host defense, phage biosynthesis rate, and phage life cycle, this paper focuses only on the optimization of phage mRNA for efficient translation. Phage mRNA may not be adapted to its host translation machinery for three reasons: (1) mutation disrupting adaptation, (2) a recent host switch leaving no time for adaptation, and (3) multiple hosts with different translation machineries so that adaptation to one host implies suboptimal adaptation to another host. It is therefore important to optimize phage mRNAs in therapeutic phages. Theoretical and practical principles based on many experiments were developed and applied to phages engineered against a drug-resistant Mycobacterium abscessus that infected a young cystic fibrosis patient. I provide a detailed genomic evaluation of the three therapeutic phages with respect to translation initiation, elongation, and termination, by making use of both experimental results and highly expressed genes in the host. For optimizing phage genes against M. abscessus, the start codon should be AUG. The D_toStart distance from base-pairing between the Shine-Dalgarno (SD) sequence and the anti-SD sequence should be 14–16. The stop codon should be UAA. If UAG or UGA is used as a stop codon, they should be followed by nucleotide U. Start codon, SD, or stop codon should not be embedded in a secondary structure that may obscure the signals and interfere with their decoding. The optimization framework should be generally applicable to developing therapeutic phages against bacterial pathogens. Full article