A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

We present fluorogenic cationic organo chalcogens that are highly selective to RNA. We have demonstrated that the conformational dynamics and subsequently the optical properties of these dyes can be controlled to facilitate efficient bioimaging. We report the application of organoselenium and organosulfur-based cell-permeable red-emissive probes bearing a favorable cyclic sidearm for selective and high contrast imaging of cell nucleoli. The probes exhibit high quantum yield upon interacting with RNA in an aqueous solution. An in-depth multiscale simulation study reveals that the prominent rotational freezing of the electron-donating sidearm of the probes in the microenvironment of RNA helps in attaining more planar conformation when compared to DNA. It exerts a greater extent of intramolecular charge transfer and hence leads to enhanced fluorescence emission. A systematic structure-interaction relationship study highlighted the impact of heavy-chalcogens toward the improved emissive properties of the probes. 

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space.

We present here a new insight into the chemical reactivity and bioactivity of dopamine (DA) and gallic acid (GA) and their hyaluronic acid (HA) conjugates. Our data suggest that HA-GA scaffolds are superior to HA-DA, with higher oxidation kinetics, improved tissue adhesive properties, and radical scavenging ability with a lower pro-inflammatory response. This journal is 

Alzheimer's disease is characterized by the accumulation of amyloid beta (Aβ) and Tau aggregates in the brain, which induces various pathological events resulting in neurodegeneration. There have been continuous efforts to develop modulators of the Aβ and Tau aggregation process to halt or modify disease progression. A few small-molecule-based inhibitors that target both Aβ and Tau pathology have been reported. Here, we report the screening of a targeted library of small molecules to modulate Aβ and Tau aggregation together with their in vitro, in silico and cellular studies. In vitro ThT fluorescence assay, dot blot assay, gel electrophoresis and transmission electron microscopy (TEM) results have shown that thiophene-based lead molecules effectively modulate Aβ aggregation and inhibit Tau aggregation. In silico studies performed by employing molecular docking, molecular dynamics and binding-free energy calculations have helped in understanding the mechanism of interaction of the lead thiophene compounds with Aβ and Tau fibril targets. In cellulo studies revealed that the lead candidate is biocompatible and effectively ameliorates neuronal cells from Aβ and Tau-mediated amyloid toxicity.