Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Efficient exploration of transition-metal decorated MXene for carbon monoxide sensing using integrated active learning and density functional theory

The urgent demand for chemical safety necessitates the real-time detection of carbon monoxide (CO), a highly toxic gas. MXene, a 2D material, has shown potential for gas sensing applications (e.g., NH3, NO, SO2, CO2) due to its high surface accessibility, electrical conductivity, stability, and flexibility in surface functionalization. However, the pristine MXene generally exhibits poor interaction with CO; still, transition metal decoration can strengthen the interaction between CO and MXene. This study presents a high-throughput screening of 450 combinations of transition-metal (TM) decorated MXene (TM@MXene) for CO sensing applications using an integrated active learning (AL) and density functional theory (DFT) screening pipeline. Our AL pipeline, adopting a crystal graph convolutional neural network (CGCNN) as a surrogate model, successfully accelerates the screening of CO sensor candidates with minimal computational resources. This study identifies Sc@Zr3C2O2 and Y@Zr3C2O2 as the optimal TM@MXene candidates with promising CO sensing performance regarding the screening criteria of recovery time, surface stability, charge transfer, and sensitivity to CO. The proposed AL framework can be extended for property finetuning in the combinatorial chemical space.

Data-Driven Discovery of Graphene-Based Dual-Atom Catalysts for Hydrogen Evolution Reaction with Graph Neural Network and DFT Calculations

The flexible tuning ability of dual-atom catalysts (DACs) makes them an ideal system for a wide range of electrochemical applications. However, the large design space of DACs and the complexity in the binding motif of electrochemical intermediates hinder the efficient determination of DAC combinations for desirable catalytic properties. A crystal graph convolutional neural network (CGCNN) was adopted for DACs to accelerate the high-throughput screening of hydrogen evolution reaction (HER) catalysts. From a pool of 435 dual-atom combinations in N-doped graphene (N₆Gr), we screened out two high-performance HER catalysts (AuCo@N₆Gr and NiNi@N₆Gr) with excellent HER, electronic conductivity, and stability using the combination of CGCNN and density functional theory (DFT). Furthermore, comprehensive DFT studies were conducted on these two catalysts to confirm their outstanding reaction kinetics and to understand the cooperative effect between the metal pair for HER. To obtain ideal hydrogen binding in AuCo, the inert Au weakens the strong hydrogen binding of Co, while for NiNi, the two weakly binding Ni cooperate. The present protocol was able to select the two catalysts with different physical origins for HER and can be applied to other DAC catalysts, which should hasten catalyst discovery.

Assisting Multitargeted Ligand Affinity Prediction of Receptor Tyrosine Kinases Associated Nonsmall Cell Lung Cancer Treatment with Multitasking Principal Neighborhood Aggregation

A multitargeted therapeutic approach with hybrid drugs is a promising strategy to enhance anticancer efficiency and overcome drug resistance in nonsmall cell lung cancer (NSCLC) treatment. Estimating affinities of small molecules against targets of interest typically proceeds as a preliminary action for recent drug discovery in the pharmaceutical industry. In this investigation, we employed machine learning models to provide a computationally affordable means for computer-aided screening to accelerate the discovery of potential drug compounds. In particular, we introduced a quantitative structure–activity-relationship (QSAR)-based multitask learning model to facilitate an in silico screening system of multitargeted drug development. Our method combines a recently developed graph-based neural network architecture, principal neighborhood aggregation (PNA), with a descriptor-based deep neural network supporting synergistic utilization of molecular graph and fingerprint features. The model was generated by more than ten-thousands affinity-reported ligands of seven crucial receptor tyrosine kinases in NSCLC from two public data sources. As a result, our multitask model demonstrated better performance than all other benchmark models, as well as achieving satisfying predictive ability regarding applicable QSAR criteria for most tasks within the model’s applicability. Since our model could potentially be a screening tool for practical use, we have provided a model implementation platform with a tutorial that is freely accessible hence, advising the first move in a long journey of cancer drug development.

Charge neutral halogen bonding tetradentate-iodotriazole macrocycles capable of anion recognition and sensing in highly competitive aqueous media

A series of neutral tetradentate halogen bonding (XB) macrocycles, comprising of two bis-iodotriazole XB donors were synthesised in 60-70% yields via a stepwise CuAAC-mediated cyclisation strategy. Extensive ¹H NMR anion titration experiments reveal halide binding affinities are critically dependent on the substitution pattern of the xylyl spacer unit. The meta-substituted macrocycle remarkably displays cooperative tetradentate XB-halide anion recognition in highly competitive 40% aqueous-organic D₂O/acetone-d₆ (40 : 60, v/v) solvent mixtures. Integration of para-xylyl and naphthyl spacer units generates extended macrocyclic cavities, capable of selective oxalate recognition. Furthermore, preliminary fluorescence exeperiments reveal dicarboxylate specific sensing can be achieved through monitoring of the naphthylene centred emission.

Hydroboration of carbonyls and imines by an iminophosphonamido tin(II) precatalyst

A novel three-coordinated tin(II) chloride [Ph₂P(N^tBu)₂]SnCl (1) supported by an N,N'-di-tert-butyliminophosphonamide having two phenyl groups on the phosphorus atom was synthesized by the reaction of the starting lithium iminophosphonamide [Ph₂P(N^tBu)₂]Li with SnCl₂·(dioxane) in toluene. The molecular structure of 1 was established by X-ray diffraction analysis. Tin(II) chloride 1 can act as an efficient precatalyst for the hydroboration of a wide variety of aldehydes, ketones, and imines at -10 °C. DFT calculations propose that hydroboration involves hydride transfer from the corresponding tin(II) hydride intermediate [Ph₂P(N^tBu)₂]SnH (10) to the carbonyl substrates via four-membered transition states (TS-12), affording three-coordinated tin(II) alkoxide intermediates [Ph₂P(N^tBu)₂]SnOR (13), followed by the stepwise reaction of 13 with HBpin (pin = pinacolate) to release the boronate esters and regenerate the tin(II) hydride 10. The stoichiometric reaction of the in site-generated 10 with benzophenone 2a at -10 °C led to the formation of 13. Moreover, 13 also stoichiometrically reacted with HBpin at -10 °C, forming the corresponding boronate ester 3a and 10 based on the ¹H NMR spectrum of the reaction mixture.