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Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and/or multi-strain antibacterial activity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurological conditions that primarily impact the elderly having distinctive traits and some similarities in terms of symptoms and progression. The multifactorial nature of AD and PD encourages exploring potentiality of multi-target therapy for addressing these conditions to conventional, the "one drug one target" strategy. This study highlights the searching of potential HDAC4 inhibitors through multiple screening approaches. In this context, structure-based pharmacophore model, ligand profiler mapping and MCDM approaches were performed for target prioritization. Similarly, ligand profiler, MCDM and Docking studies were performed to prioritize multi-targeted HDAC4 inhibitors. These comprehensive approaches unveiled 5 common targets and 5 multi-targeted prioritized compounds consensually. MD simulations, DFT and binding free energy calculations corroborated the stability and robustness of propitious compound 774 across 5 prioritized targets. In conclusion, the screened compound 774 (ChEMBL 4063938) could be a promising multi-targeted therapy for managing AD and PD further rendering experimental validation. [ABSTRACT FROM AUTHOR]

Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and/or multi-strain antibacterial activity.

Antimicrobial photodynamic inactivation (aPDI), using photosensitisers in combination with antibiotics, is a promising multi-target strategy to address antibiotic resistance, particularly in wound infections. This study aimed to elucidate the antibacterial mode of action of combinations of berberine (Ber) or curcumin (Cur) with selected antibiotics (Ber-Ab or Cur-Ab) under blue light irradiation (420 nm) against Staphylococcus aureus, including methicillin-resistant (MRSA) and methicillin-susceptible (MSSA) strains. Multiple physiological parameters were assessed using complementary assays (fluorometry, epifluorescence microscopy, flame emission and atomic absorption spectroscopy, zeta potential, flow cytometry, and the plate agar method) to examine the effect on ROS production, membrane integrity, DNA damage, motility and virulence factors of S. aureus. Results indicated that blue light photoactivated Ber-Ab and Cur-Ab combinations led to substantial ROS generation, even at low concentrations, causing oxidative stress that severely impacted bacterial membrane integrity (approximately 90 % in MRSA and 40 % in MSSA). Membrane destabilization was further confirmed by elevated intercellular potassium release (≈ 2.00 and 2.40 µg/mL in MRSA and MSSA, respectively). Furthermore, significant DNA damage was observed in both strains (≈ 50 %). aPDI treatment with blue light also reduced S. aureus pathogenicity by impairing motility and inhibiting key virulence factors such as proteases, lipases, and gelatinases, all of which play key roles in the infectious process. Overall, Ber-Ab combinations demonstrated the highest efficacy across all parameters tested, highlighting for the first time the multi-target therapeutic potential of this phytochemical-based aPDI strategy to combat antibiotic-resistant S. aureus infections and improve wound infection treatment outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier B.V.)