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The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610-0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520-0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119-0.148, MAE-LOOTRAIN = 0.132-0.177, predictive R2 or Q2F1 = 0.538-0.567, Q2F2 = 0.500-0.565, MAETEST = 0.122-0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (-ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.

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In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils.

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The mission of the Force Health Protection (FHP) program of the U.S. Air Force (USAF), sustaining the readiness of warfighters, relies on determinations of acceptable levels of exposure to a wide array of substances that USAF personnel may encounter. In many cases, exposure details are limited or authoritative toxicity reference values (TRVs) are unavailable. To address some of the TRV gaps, we are integrating several approaches to generate health protective exposure guidelines. Descriptions are provided for identification of chemicals of interest for USAF FHP (467 to date), synthesis of multiple TRVs to derive Operational Exposure Limits (OpELs), and strategies for identifying and developing candidate values for provisional OpELs when authoritative TRVs are lacking. Rodent bioassay-derived long-term Derived No Effect Levels (DNELs) for workers were available only for a minority of the substances with occupational TRV gaps (19 of 84). Additional occupational TRV estimation approaches were found to be straightforward to implement: Tier 1 Occupational Exposure Bands, cheminformatics approaches (multiple linear regression and novel nearest-neighbor approaches), and empirical adjustment of short term TRVs. Risk assessors working in similar contexts may benefit from application of the resources referenced and developed in this work.

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Force Health Protection programs in the U.S. Air Force endeavor to sustain the operational readiness of the warfighters. We have previously identified hundreds of chemical substances of interest and toxicity reference value (TRV) knowledge gaps that constrain risk based-decision-making for potential exposures. Multiple approaches to occupational TRV estimation were used to generate possible guideline values for 84 compounds (18% of the substances of interest). These candidate TRVs included values from international databases, chemical similarity (nearest neighbor) approaches, empirical adjustments to account for duration differences, quantitative activity relationships, and thresholds of toxicological concern. This present work describes derivation of provisional TRVs from these candidate values. Rodent bioassay-derived long-term worker Derived No-Effect Levels (DNELs) were deemed presumptively the most reliable, but only 19 such DNELs were available for the 84 substances with TRV gaps. In the absence of DNELs, the quality of the approaches and consistency among candidate values were key elements of the weight of evidence used to select the most suitable guideline values. The use of novel nearest-neighbor approaches, empirical adjustment of short term TRVs, and occupational exposure bands were found to be options that would allow occupational TRV estimation with reasonable confidence for nearly all substances evaluated.

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ß-lactam antibiotics, extensively used worldwide, pose significant risks to human health and ecological safety due to their accumulation in the environment. Recent studies have demonstrated the efficacy of transition metal-activated sulfite systems, like Fe(Ⅲ)/HSO3-, in removing PPCPs from water. However, research on their capability to degrade ß-lactam antibiotics remains sparse. This paper evaluates the degradation of 14 types of ß-lactam antibiotics in Fe(Ⅲ)/HSO3- system and establishes a QSAR model correlating molecular descriptors with degradation rates using the MLR method. Using cefazolin as a case study, this research predicts degradation pathways through NPA charge and Fukui function analysis, corroborated by UPLC-MS product analysis. The investigation further explores the influence of variables such as HSO3- dosage, substrate concentration, Fe(Ⅲ) dosage, initial pH and the presence of common seen water matrices including humic acid and bicarbonate on the degradation efficiency. Optimal conditions for cefazolin degradation in Fe(Ⅲ)/HSO3- system were determined to be 93.3 µM HSO3-, 8.12 µM Fe(Ⅲ) and an initial pH of 3.61, under which the interaction of Fe(Ⅲ) dosage with initial pH was found to significantly affect the degradation efficiency. This study not only provides a novel degradation approach for ß-lactam antibiotics but also expands the theoretical application horizon of the Fe(Ⅲ)/HSO3- system.

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Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood-brain barrier (BBB) permeability prediction, the quantitative structure-activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.

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Technical complexity associated with biodegradation testing, particularly for substances of unknown or variable composition, complex reaction products, or biological materials (UVCB), necessitates the advancement of non-testing methods such as quantitative structure-property relationships (QSPRs). Models for describing the biodegradation of petroleum hydrocarbons (HCs) have been previously developed. A critical limitation of available models is their inability to capture the variability in biodegradation rates associated with variable test systems and environmental conditions. Recently, the Hydrocarbon Biodegradation System Integrated Model (HC-BioSIM) was developed to characterize the biodegradation of HCs in aquatic systems with the inclusion of key test system variables. The present study further expands the HC-BioSIM methodology to soil and sediment systems using a database of 2195 half-life (i.e., degradation time [DT]50) entries for HCs in soil and sediment. Relevance and reliability criteria were defined based on similarity to standard testing guidelines for biodegradation testing and applied to all entries in the database. The HC-BioSIM soil and sediment models significantly outperformed the existing biodegradation HC half-life (BioHCWin) and virtual evaluation of chemical properties and toxicities (VEGA) quantitative Mario Negri Institute for Pharmacological Research (IRFMN) models in soil and sediment. Average errors in predicted DT50s were reduced by up to 6.3- and 8.7-fold for soil and sediment, respectively. No significant bias as a function of HC class, carbon number, or test system parameters was observed. Model diagnostics demonstrated low variability in performance and high consistency of parameter usage/importance and rule structure, supporting the generalizability and stability of the models for application to external data sets. The HC-BioSIM provides improved accuracy of Persistence categorization, with correct classification rates of 83.9%, and 90.6% for soil and sediment, respectively, demonstrating a significant improvement over the existing BioHCWin (70.7% and 58.6%) and VEGA (59.5% and 18.5%) models. Environ Toxicol Chem 2024;43:1352-1363. © 2024 Concawe. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;43:1161-1172. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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The development of accurate and interpretable models for predicting reaction constants of organic compounds with hydroxyl radicals is vital for advancing quantitative structure-activity relationships (QSAR) in pollutant degradation. Methods like molecular descriptors, molecular fingerprinting, and group contribution methods have limitations, as traditional machine learning struggles to capture all intramolecular information simultaneously. To address this, we established an integrated graph neural network (GNN) with approximately 12 million learnable parameters. GNN represents atoms as nodes and chemical bonds as edges, thus transforming molecules into a graph structures, effectively capturing microscopic properties while depicting atom connectivity in non-Euclidean space. Our datasets comprise 1401 pollutants to develop an integrated GNN model with Bayesian optimization, the model achieves root mean square errors of 0.165, 0.172, and 0.189 on the training, validation, and test datasets, respectively. Furthermore, we assess molecular structure similarity using molecular fingerprint to enhance the model's applicability. Afterwards, we propose a gradient weight mapping method for model explainability, uncovering the key functional groups in chemical reactions in artificial intelligence perspective, which would boost chemistry through artificial intelligence extreme arithmetic power.

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Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

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Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

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Large repositories of in vitro bioactivity data such as US EPA's Toxicity Forecaster (ToxCast) provide a wealth of publicly accessible toxicity information for thousands of chemicals. These data can be used to calculate point-of-departure (POD) estimates via concentration-response modeling that may serve as lower bound, protective estimates of in vivo effects. However, the data are predominantly based on mammalian models and discussions to date about their utility have largely focused on potential integration into human hazard assessment, rather than application to ecological risk assessment. The goal of the present study was to compare PODs based on (1) quantitative structure-activity relationships (QSARs), (2) the 5th centile of the activity concentration at cutoff (ACC), and (3) lower-bound cytotoxic burst (LCB) from ToxCast, with the distribution of in vivo PODs compiled in the Ecotoxicology Knowledgebase (ECOTOX). While overall correlation between ToxCast ACC5 and ECOTOX PODs for 649 chemicals was weak, there were significant associations among PODs based on LCB and ECOTOX, LCB and QSARs, and ECOTOX and QSARs. Certain classes of compounds showed moderate correlation across datasets (eg, antimicrobials/disinfectants), while others, such as organophosphate insecticides, did not. Unsurprisingly, more precise classifications of the data based on ECOTOX effect and endpoint type (eg, apical vs biochemical; acute vs chronic) had a significant effect on overall relationships. Results of this research help to define appropriate roles for data from new approach methodologies in chemical prioritization and screening of ecological hazards.

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Polycyclic aromatic compounds (PACs) present in the water column are considered to be one of the primary contaminant groups contributing to the toxicity of a crude oil spill. Because crude oil is a complex mixture composed of thousands of different compounds, oil spill models rely on quantitative structure-activity relationships like the target lipid model to predict the effects of crude oil exposure on aquatic life. These models rely on input provided by single species toxicity studies, which remain insufficient. Although the toxicity of select PACs has been well studied, there is little data available for many, including transformation products such as oxidized hydrocarbons. In addition, the effect of environmental influencing factors such as temperature on PAC toxicity is a wide data gap. In response to these needs, in the present study, Stage I lobster larvae were exposed to six different understudied PACs (naphthalene, fluorenone, methylnaphthalene, phenanthrene, dibenzothiophene, and fluoranthene) at three different relevant temperatures (10, 15, and 20 °C) all within the biological norms for the species during summer when larval releases occur. Lobster larvae were assessed for immobilization as a sublethal effect and mortality following 3, 6, 12, 24, and 48 h of exposure. Higher temperatures increased the rate at which immobilization and mortality were observed for each of the compounds tested and also altered the predicted critical target lipid body burden, incipient median lethal concentration, and elimination rate. Our results demonstrate that temperature has an important influence on PAC toxicity for this species and provides critical data for oil spill modeling. More studies are needed so oil spill models can be appropriately calibrated and to improve their predictive ability. Environ Toxicol Chem 2023;42:2389-2399. © 2023 SETAC.

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Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

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This study aimed to evaluate and monitor pesticides in groundwater of the Serra Geral aquifer, located in the Paraná Basin 3 (southern Brazil), using Liquid Chromatography coupled with a Quadrupole-Time-of-Flight Mass Spectrometer (LC-QTOF MS). A total of 117 samples, collected in three different moments, were analyzed over 36 months. Groundwater samples from 35 wells and four surface water points were monitored in each sampling campaign. A pesticide screening methodology was proposed with the tentative identification of 1607 pesticides and pesticide metabolites. The application of the proposed methodology resulted in the verification of 29 pesticides and pesticide metabolites, 7 as confirmed analytes and 22 as suspect compounds. (Q)SAR in silico predictions and GUS index calculation provided data on the potential environmental risk of the identified compounds, with eight endpoints considered. After in silico predictions, an alternative hybrid multicriteria method was applied, combining the weighting of endpoints of fuzzy AHP and micropollutants classification by environmental risk using ELECTRE. The fuzzy AHP results indicated the greatest importance of mutagenicity among the eight evaluated indicators, while the scarce influence of the physicochemical properties on the environmental risk suggested their exclusion from the model. Accordingly, the ELECTRE results highlighted the prevalence of thiamethoxam and carbendazim as the most dangerous for the environment. The application of the proposed method enabled the selection of the compounds that must be monitored, considering mutagenicity and toxicity predictions for environmental risk analysis.

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Aim: To develop a one-dimensional convolutional neural network-based quantitative structure-activity relationship (1D-CNN-QSAR) model to identify novel anthrax inhibitors and analyze chemical space. Methods: We developed a 1D-CNN-QSAR model to identify novel anthrax inhibitors. Results: The statistical results of the 1D-CNN-QSAR model showed a mean square error of 0.045 and a predicted correlation coefficient of 0.79 for the test set. Further, chemical space analysis showed more than 80% fragment pair similarity, with activity cliffs associated with carboxylic acid, 2-phenylfurans, N-phenyldihydropyrazole, N-phenylpyrrole, furan, 4-methylene-1H-pyrazol-5-one, phenylimidazole, phenylpyrrole and phenylpyrazolidine. Conclusion: These fragments may serve as the basis for developing potent novel drug candidates for anthrax. Finally, we concluded that our proposed 1D-CNN-QSAR model and fingerprint analysis might be used to discover potential anthrax drug candidates.

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Quantitative structure-activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi.nlm.nih.gov/] database provided the geometries, binding affinities(Ki ) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD50 ) to breast cancer cells. An innovative quantum similarity approach combining (self)-similarity indexes calculated with different charge-fitting schemes under the Topo-Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R2 ) and leave-one-out cross-validation[Q2 (LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD50 : R2 =0.9666 and Q2 (LOO)=0.9312; pKi (CB1): R2 =1.0000 and Q2 (LOO)=0.9727, and pKi (CB2): R2 =0.9996 and Q2 (LOO)=0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similarity-based descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D-QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand-based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.

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The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect.

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Oil spill risk and impact assessments rely on time-dependent toxicity models to predict the hazard of the constituents that comprise crude oils and petroleum substances. Dissolved aromatic compounds (ACs) are recognized as a primary driver of aquatic toxicity in surface spill exposure scenarios. However, limited time-dependent toxicity data are available for different classes of ACs to calibrate such models. This study examined the acute toxicity of 14 ACs and 3 binary AC mixtures on Artemia franciscana nauplii at 25 °C. Toxicity tests for 3 ACs were also conducted at 15 °C to evaluate the role of temperature on toxicity. The ACs investigated represented parent and alkylated homocyclic and nitrogen-, sulfur- and oxygen-containing heterocyclic structures with octanol-water partition coefficients (log Kow) ranging from 3.2 to 6.6. Passive dosing was used to expose and maintain concentrations in toxicity tests which were confirmed using fluorometry, and independently validated for 6 ACs using GC-MS analysis. Mortality was assessed at 6, 24, and 48 h to characterize the time course of toxicity. No mortality was observed for the most hydrophobic AC tested, 7,12-dimethylbenz[a]anthracene, due to apparent water solubility constraints. Empirical log LC50 s for the remaining ACs were fit to a linear regression with log Kow to derive a critical target lipid body burden (CTLBB) based on the target lipid model. The calculated 48 h CTLBB of 47.1 ± 8.1 µmol/g octanol indicates that Artemia nauplii exhibited comparable sensitivity to other crustaceans. A steep concentration-response was found across all compounds as evidenced by a narrow range (1.0-3.1) in the observed LC50 /LC10 ratio. Differences in toxicokinetics were noted, and no impacts of temperature-dependence of AC toxicity were found. Toxicity data obtained for individual ACs yielded acceptable predictions of observed binary AC mixture toxicity. Results from this study advance toxicity models used in oil spill assessments.

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RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

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