@article {48775,
	title = {MeGen-generation of gallium metal clusters using reinforcement learning},
	journal = {Machine Learning-Science and Technology},
	volume = {4},
	year = {2023},
	month = {JUN },
	pages = {025032},
	type = {Article},
	abstract = {<p>
	The generation of low-energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter-atomic interaction description. In this work, we formulate the search algorithm as a reinforcement learning (RL) problem. Concisely, we propose a novel actor-critic architecture that generates low-lying isomers of metal clusters at a fraction of computational cost than conventional methods. Our RL-based search algorithm uses a previously developed DART model as a reward function to describe the inter-atomic interactions to validate predicted structures. Using the DART model as a reward function incentivizes the RL model to generate low-energy structures and helps generate valid structures. We demonstrate the advantages of our approach over conventional methods for scanning local minima on potential energy surface. Our approach not only generates isomer of gallium clusters at a minimal computational cost but also predicts isomer families that were not discovered through previous density-functional theory (DFT)-based approaches.</p>
},
	keywords = {gallium clusters, reinforcement learning, structure generation},
	doi = {10.1088/2632-2153/acdc03},
	author = {Modee, Rohit and Verma, Ashwini and Joshi, Kavita and Priyakumar, U. Deva}
}
@article {50039,
	title = {Mechanistic principles of antimicrobial peptides uncovered by charge density-based machine learning},
	journal = {Chemical Communications},
	volume = {62},
	year = {2026},
	month = {FEB},
	pages = {PMID 9610838},
	type = {Article},
	abstract = {<p>
	Antimicrobial peptides (AMPs) are emerging as potent alternatives to conventional antibiotics, yet their diverse nature due to divergent mechanisms of action hinders rational design. Here, we present an electrostatics-stratified computational framework that uncovers key physicochemical principles governing AMP activity. Experimentally validated peptides were grouped by average charge per residue (i.e., the charge/length of the peptide) and analyzed through integrated sequence-, structure-, and chemistry-based descriptors. Distinct molecular signatures emerged across electrostatic regimes: low-charge/length peptides rely on amphipathic organization via structural compactness, whereas the intermediate-charge/length peptides exhibit balanced hydrophobicity and electrostatics. The high-charge peptides couple strong cationic attraction with lipophilicity and tryptophan anchoring to mainly disrupt membranes. Interestingly, hydrophobic moment, which is a measure of the amphipathicity, is found to be important in all three classes of AMPs. This study identifies distinguishing features of AMP sub-groups and suggests design guidelines for developing selective and potent next-generation AMPs.</p>
},
	issn = {1359-7345},
	doi = {10.1039/d5cc06374d},
	author = {Malshikare, Hrushikesh and Priyakumar, U. Deva and Chatterjee, Prathit and Sengupta, Durba}
}
