Machine Learning in Antibacterial Drug Design

Abstract

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.

Potent antibacterial activity of MXene–functionalized graphene nanocomposites

“…In silico identication of bacterial resistance mechanisms, and genes can be integrated with medicinal chemistry and nanomaterials science, so that MXene-graphene 2D nanocomposites can be made even more specic to be tailored to disrupt a particular bacterial serovar, or serotypes. 53 The rationale of this study was literature precedent showing superior antibacterial activity of 2D nanomaterials and to explore opportunities for further optimization. 2D nanomaterials have ultrathin thickness, (normally) low toxicity (subject to synthetic methodology/purity/etc.…”

“…In silico identification of bacterial resistance mechanisms, and genes can be integrated with medicinal chemistry and nanomaterials science, so that MXene–graphene 2D nanocomposites can be made even more specific to be tailored to disrupt a particular bacterial serovar, or serotypes. 53 …”

Antibiotic discovery in the artificial intelligence era

“…Similarly, increased democratization of ML and DL resources for new antibiotic discovery will be instrumental in advancing antibiotic research toward our collective goals. ML approaches and tools are becoming widely accessible 171,172 even to those without strong computational expertise. However, unlike some fields that have large and reasonably well-controlled publicly available datasets, antibacterial research is somewhat lacking in the quantity and methodological transparency of easily accessible data.…”

Advances in computational frameworks in the fight against TB: The way forward

“…ML algorithms have been used before to screen out small molecules, natural compounds and antibacterial peptides from established databases. Molecular descriptors and fingerprints along with SMILE strings (of the drug candidates) were key input formats for model development and deployment ( Jukič & Bren, 2022 ). Graphical properties, like molecular shape indices and molecular connectivity indices have been used previously to represent physiochemical properties of small compounds.…”