## 1 Introduction  
Bacterial infections have always been widely acknowledged as life-threatening medical conditions, being related to high morbidity and mortality, as well as ever-increasing economic costs in terms of medical treatment options. A 2022 epidemiological study reported the occurrence of 13.7 million infection-related deaths; more than half of these deaths involved 33 pathogenic bacteria [1]. Antibacterial agents have been essential for fighting and eradicating bacterial infections, but their discovery and development have been impeded mainly by two factors. On one side, current antibacterial agents/antibiotics exert their therapeutic activity by acting through single mechanisms of action. Therefore, they have become less effective due to the emergence and development of multi-drug resistance (MDR) [2]. It is important to highlight that MDR is a very complex biological phenomenon that has been increasing over time and has shown no signs of slowing down [3]. In this sense, MDR is now a very common phenomenon among both Gram-positive [4] and Gram-negative [5] bacteria, with the latter also exhibiting intrinsic MDR to current antibacterial agents/antibiotics [6]. On the other hand, it is now well known that antibacterial discovery has become a therapeutic area with a remarkably low lucrative value. As a result, many pharmaceutical companies are abandoning their antibacterial discovery projects, which seems to be associated with limited success and budget constraints [2; 7]. This has led to the emergence of initiatives such as the Community for Open Antimicrobial Drug Discovery (CO-ADD) [8; 9; 10], whose purpose is to conduct a worldwide and free-of-charge high throughput screening of untested chemical libraries against a key panel of drug-resistant bacterial strains (_E. coli_, _K. pneumoniae_, _Acinetobacter baumannii_, _P. aeruginosa_, and methicillin-resistant _S. aureus_) [8; 11]. In any case, searching for novel and versatile antibacterial agents capable of coping with the ever-increasing infections and development of MDR is an imperative need. Computational approaches are widely recognized as pillars of the drug discovery process [12]. In the context of antibacterial discovery, pharmacophore modeling [13; 14; 15], molecular dynamic simulations [13; 14; 15; 16; 17; 18], quantum mechanical methods [17; 19; 20], molecular docking [13; 15; 16; 17; 18; 19; 20; 21; 22], and _in silico_ models based on machine learning techniques [18; 22; 23; 24; 25] continue to be at the forefront regarding the study and analysis of novel antibacterial agents. Yet, these computational approaches have one or more of the following major limitations. First, they usually focus on the modeling of very small datasets of chemically similar molecules with low structural variability, thus preventing a deeper and more insightful exploration of the chemical space to search for antibacterial agents. Second, physicochemical and structural interpretation is often insufficient, and therefore, it is not possible to establish adequate guidelines on the design of new chemotypes exhibiting antibacterial activity. Last and most important, it is well established that, as a process, drug discovery (and, in this context, the identification of novel and versatile antibacterial molecules) is a multi-objective optimization (MOOP) problem [26; 27]. It is important to note that MOOP involves the identification of chemicals that simultaneously display potent activity (at both target-based and phenotypic levels), as well as reduced toxicity and adequate pharmacokinetic profiles [28]. All the computational approaches mentioned above have been almost uniquely applied to single-objective optimization, focusing solely on antibacterial activity against a single biological target (protein or bacterial strain); this is detrimental because it impedes the possibility of identifying versatile (multi-target and/or multi-strain) antibacterial agents capable of tackling MDR.