Volume-2

First published on the web on Sep 14, 2026

About Cover Image: Interleukin-1 beta (IL-1β) (PDB: 5R8Q) docked with compound 5750-2701 in the active site.

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Structure-Based Drug Design and Machine Learning Integration to Identify Potential YKL‑40 Inhibitors for Neutrophilic Asthma

Neutrophilic asthma is a severe, corticosteroid‑resistant subtype of asthma characterized by elevated airway neutrophils and chronic inflammation. YKL‑40 (CHI3L1), a glycoprotein secreted by neutrophils, macrophages, and epithelial cells, has emerged as a key mediator of airway remodeling, inflammation, and steroid resistance. Despite its clinical relevance, no selective small‑molecule inhibitors for YKL‑40 currently exist. This study integrates structure‑based drug design with machine learning (ML) to identify potential YKL‑40 inhibitors. Using Schrödinger Maestro for protein preparation, binding pocket identification, and Glide docking, combined with ML‑based prescreening for toxicity and anti‑infective risk, we screened diverse chemical libraries to identify high‑affinity binders. Three compounds demonstrated exceptional docking scores (–13.816, –12.429, and –12.024 kcal/mol), surpassing previously reported inhibitors (–8.876 kcal/mol). Interaction analyses revealed extensive hydrogen bonding, hydrophobic contacts, and π‑stacking interactions within the YKL‑40 active site. ML‑based toxicity filtering further prioritized compounds with favorable safety profiles. These findings highlight the potential of YKL‑40 as a druggable target and demonstrate the effectiveness of integrating ML with molecular docking for accelerated inhibitor discovery.

Saumil Agarwal and Divya Ramamoorthy

The Bird That Silenced the Bullet: How a Kingfisher’s Beak Tamed the "Tunnel Boom"

Samved Sajja

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder that affects the frontal and temporal lobes, leading to abnormalities in behavior, personality, language, and executive function. Reduced progranulin (PGRN) levels represent a critical pathological factor in FTD. The crystal structure of the Sortilin-PGRN complex (PDB ID: 8T8S) reveals a 16-residue PGRN-derived peptide bound within the VPS10 domain of Sortilin. Three compound libraries were evaluated: a CNS-focused library from the SELECT database, a peptidomimetic library, and a library generated using MoleView Substructure Search. All compounds were docked using Glide standard precision, followed by re-docking of top candidates using Glide extra precision. None of the screened compounds achieved docking scores comparable to known Sortilin inhibitors, which ranged from -7.520 to -8.975 kcal/mol. Screened compounds scored between -6.115 and -5.495 kcal/mol, reflecting the inherent difficulty of designing small-molecule inhibitors for a peptide-binding interface. This computational screening study identified multiple small-molecule candidates capable of engaging the Sortilin-PGRN binding site.

Structure-Based Drug Discovery Targeting the Sortilin-Progranulin Interaction in Frontotemporal Dementia

Nethra Sriram and Divya Ramamoorthy

This study explores how the aerodynamic shape of the Kingfisher’s beak inspired the design of the Shinkansen bullet train to mitigate the pressure-driven "tunnel boom" phenomenon. By utilizing a machine learning classification model to identify train types and analyzing tunnel pressure data, the research demonstrates that a beak-inspired locomotive hood significantly reduces air pressure during high-speed transit.

Structure-Based Drug Design to Discover Potential Hit Molecules for the IL-1β protein to prevent Dry Age-Related Macular Degeneration

Jamie Ho and Divya Ramamoorthy

Age-related Macular Degeneration (AMD) is an eye disease characterized by chronic inflammation and progressive retinal cell death. This damage to the macula, the region of the retina responsible for sharp, straight-ahead vision, leads to central vision loss. This study will examine Dry AMD, a chronic inflammatory condition of the retina, particularly in the retinal pigment epithelium (RPE) and photoreceptors, which is associated with elevated IL-1β expression, a pro-inflammatory cytokine secreted by activated immune cells during early AMD pathologic events. This study utilizes structure-based drug design to identify potential hit molecules targeting the active site of the NHL domain of the IL-1β protein. Using the Schrödinger Virtual Work Space and docking simulation, we screened the Ocular Library and redesigned the ligand found in the 5R8Q structure of IL-1β to identify favorable binding energies and drug-likeness. We identified 5750-2701 (1-[4-amino-6-(4-methoxyanilino)-1,3,5-triazin-2-yl]-3-[(4-methylphenyl)sulfanyl]propan-2-one) as a promising candidate, exhibiting strong binding interactions and low toxicity. These findings offer a potential therapeutic pathway to prevent the chronic inflammatory process of AMD.

Computational Drug Discovery to Identify Novel Inhibitors for Human PINK1 in Early-Onset Parkinson's Disease

Hrishikesh Satish and Divya Ramamoorthy

Parkinson's Disease (PD) is a progressive neurodegenerative disorder linked to the loss of dopaminergic neurons in the substantia nigra. Early-onset PD has been associated with loss-of-function mutations in PINK1, a mitochondrial kinase that, in concert with Parkin, targets damaged mitochondria for degradation via ubiquitination. Disruption of this pathway leads to accumulation of dysfunctional mitochondria, protein misfolding, Lewy body formation, and subsequent neurodegeneration. Despite its established role in PD pathogenesis, no inhibitors have been characterized for the human PINK1 variant (PDB: 6EQI). In this study, we employed a computational drug discovery pipeline using the Schrödinger Suite 2024-3 to identify novel small-molecule inhibitors of human PINK1. Known inhibitors from a published study by Rasool et al. on insect-variant PINK1 structures (8UCT, 8UDC) were docked against 6EQI as reference compounds, alongside compounds sourced from the Selleck CNS Database and a substructure search derived from the pharmacophore of PRT062607. Glide SP and XP docking, followed by QikProp ADME analysis, identified compound 57471874 as the top-scoring novel hit with a docking score of −7.913 kcal/mol, surpassing all Rasool et al. reference compounds. ADME profiling demonstrated that the CNS database hits C23H16FNO2 and C21H24N4 exhibited markedly superior predicted blood-brain barrier permeability (qLogBB +0.08 and +0.06, respectively) and oral absorption (100% and 96.1%) relative to the Rasool reference set, while retaining competitive docking scores. These findings identify a promising novel chemotype for hit-to-lead optimization targeting human PINK1, with implications for the development of CNS-penetrant, disease-modifying therapeutics for early-onset Parkinson's Disease.

From Code to Consciousness: The Evolution of Physical AI

Himasri Gurram

This study investigates the rapid acceleration of humanoid robot development by analyzing the release and funding data of 150 different models over the past decade. Utilizing a linear regression model with 92% accuracy, the research reveals an exponential surge in production—projecting over 90 new robot releases by 2026—and highlights an imminent shift toward everyday human-robot collaboration.

Computational Drug Discovery to Identify Inhibitors of GNAS Protein in Pancreatic Cancer

Enoch Chui and Divya Ramamoorthy

Pancreatic cancer is one of the most lethal diseases worldwide, characterized by aggressive progression and a low survival rate. A critical factor in its development is the abnormal activation of the GNAS protein, a stimulatory G-protein alpha subunit that regulates cell growth via the adenylyl cyclase-cAMP-PKA pathway. When constitutively active (GTP-bound), GNAS drives malignant signaling, making it a critical therapeutic target. This study utilizes structure-based drug design and molecular docking to investigate potential inhibitors that target both the active (PDB: 7BPH) and inactive (PDB: 7E5E) states of GNAS. Using the Schrödinger Suite, we screened compounds from a Peptidomimetics library and the Selleck anti-cancer library. Molecular docking revealed that several peptidomimetic compounds showed favorable binding affinity to the active GTP-bound GNAS, with docking scores reaching -5.689 kcal/mol. Additionally, AR-42, an anti-cancer compound, demonstrated a strong predicted binding affinity of -8.071 kcal/mol for the inactive GDP-bound state. These computational findings offer a foundational framework for identifying molecular targets that could stabilize specific GNAS conformations, potentially disrupting the dysregulated signaling pathways in pancreatic cancer and guiding future experimental validation.