AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform.

The widespread proliferation of artificial intelligence (AI) and machine learning (ML) methods has a profound effect on the drug discovery process. However, many scientists are reluctant to utilize these powerful tools due to the steep learning curve typically associated with them. AIDDISON offers a convenient, secure, web-based platform for drug discovery, addressing the reluctance of scientists to adopt AI and ML methods due to the steep learning curve. By seamlessly integrating generative models, ADMET property predictions, searches in vast chemical spaces, and molecular docking, AIDDISON provides a sophisticated platform for modern drug discovery. It enables less computer-savvy scientists to utilize these powerful tools in their daily activities, as demonstrated by an example of identifying a valuable set of molecules for lead optimization. With AIDDISON, the benefits of AI/ML in drug discovery are accessible to all.

Prospective Assessment of Virtual Screening Heuristics Derived Using a Novel Fusion Score.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Rapid Identification of Novel Inhibitors of the Human Aquaporin-1 Water Channel.

Aquaporins (AQPs) are a family of membrane proteins that function as channels facilitating water transport in response to osmotic gradients. These play critical roles in several normal physiological and pathological states and are targets for drug discovery. Selective inhibition of the AQP1 water channel may provide a new approach for the treatment of several disorders including ocular hypertension/glaucoma, congestive heart failure, brain swelling associated with a stroke, corneal and macular edema, pulmonary edema, and otic disorders such as hearing loss and vertigo. We developed a high-throughput assay to screen a library of compounds as potential AQP1 modulators by monitoring the fluorescence dequenching of entrapped calcein in a confluent layer of AQP1-overexpressing CHO cells that were exposed to a hypotonic shock. Promising candidates were tested in a Xenopus oocyte-swelling assay, which confirmed the identification of two lead classes of compounds belonging to aromatic sulfonamides and dihydrobenzofurans with IC50 s in the low micromolar range. These selected compounds directly inhibited water transport in AQP1-enriched stripped erythrocyte ghosts and in proteoliposomes reconstituted with purified AQP1. Validation of these lead compounds, by the three independent assays, establishes a set of attractive AQP1 blockers for developing novel, small-molecule functional modulators of human AQP1.

In vivo optimization of 2,3-diaminopyrazine Rho Kinase inhibitors for the treatment of glaucoma.

A series of 2,3,6-pyrazine Rho Kinase inhibitors were optimized for in vivo activity for topical ocular dosing. Modifications of the 2-(piperazin-1-yl)pyrazine derivatives produced compounds with improved solubility and physicochemical properties. Modifications of the 6-pyrazine substituent led to improvements in in vitro potency. Compound 9 had the best in vitro and in vivo potency of EC50=260 nM with a 30% reduction of IOP in a non-human primate model at a dose of 0.33%.

Developing descriptors to predict mechanical properties of nanotubes.

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

2,3-Diaminopyrazines as Rho kinase inhibitors.

Inhibition of rho kinase (ROCK) has been recognized as an important target for a number of diseases, including glaucoma. Herein we report SAR development around two hits from a kinase library that led to the discovery of the ROCK inhibitor compound 38. In vitro and in vivo analysis of this compound, including its effects in a monkey model of glaucoma will be discussed.

Ocular pharmacokinetics of moxifloxacin after topical treatment of animals and humans.

The ocular penetration and pharmacokinetics of moxifloxacin in comparison to other fluoroquinolones (ofloxacin, ciprofloxacin, gatifloxacin, norfloxacin, levofloxacin, and lomefloxacin) have been determined by in vitro and ex vivo techniques, as well as in animal and human studies. This article reviews the original pharmacokinetics work performed by Alcon and other studies reported in the ocular fluoroquinolone literature. The results consistently demonstrate higher maximum concentrations for moxifloxacin relative to the other fluoroquinolones in ocular tissues with levels well above its minimum inhibitory concentrations for relevant ocular pathogens. This superior performance is due to the unique structure of moxifloxacin that combines high lipophilicity for enhanced corneal penetration with high aqueous solubility at physiological pH. The latter property creates a high concentration gradient at the tear film/corneal epithelial interface providing a driving force for better ocular penetration for moxifloxacin. In addition, the higher concentration of moxifloxacin in VIGAMOX (i.e., 0.5% vs. 0.3%) allows more antibiotic to be available to ocular tissues. It is clear from the array of studies summarized in this report that moxifloxacin penetrates ocular tissues better (two- to three-fold) than gatifloxacin, ciprofloxacin, ofloxacin, or levofloxacin. This consistent, enhanced penetration of topical moxifloxacin offers powerful advantages for ophthalmic therapy.

A novel and selective 5-HT2 receptor agonist with ocular hypotensive activity: (S)-(+)-1-(2-aminopropyl)-8,9-dihydropyrano[3,2-e]indole.

Serotonin 5-HT2 receptor agonists have recently been shown to be effective in lowering intraocular pressure in nonhuman primates and represent a potential new class of antiglaucoma agents. As part of an effort to identify new selective agonists at this receptor, we have found that (S)-(+)-1-(2-aminopropyl)-8,9-dihydropyrano[3,2-e]indole (AL-37350A, 11) has high affinity and selectivity (>1000-fold) for the 5-HT(2) receptor relative to other 5-HT receptors. More specifically, 11 is a potent agonist at the 5-HT2A receptor (EC50 = 28.6 nM, E(max) = 103%) that is comparable to serotonin. Evaluation of 11 in conscious ocular hypertensive cynomolgus monkeys showed this compound to be efficacious in reducing intraocular pressure (13.1 mmHg, -37%). Thus, 11 is a potent full agonist with selectivity for the 5-HT2 receptor and is anticipated to serve as a useful tool in exploring the role of the 5-HT2 receptor and its effector system in controlling intraocular pressure.

Optimization of focused chemical libraries using recursive partitioning.

A number of methods currently exist for designing chemical libraries. General or universal libraries use a measurement of chemical diversity in their design and seek to cover as much of chemical space as possible in order to maximize the likelihood of discovering a novel lead class of active compounds. Focused chemical libraries are then synthesized to expand on this particular class and thoroughly explore the space about it. Rarely, however, is relevant biological data tightly incorporated in the design of focused libraries. Recursive partitioning is a statistical technique that is used to quickly build SAR models from high-throughput screening data sets and associated chemical descriptors. Using these models in a virtual screening mode significantly increases the probability of finding other active compounds. The predicted activity can be also be used as the fitness function for a genetic algorithm that is designed to select monomer subsets having a higher probability of being active. This dramatically reduces the number of compounds that need to be synthesized in focused libraries thus saving considerable time, effort and expense. This paper describes how recursive partitioning models are used to optimize the design of focused chemical libraries.