Structural characterization and keto-enol tautomerization of 4-substituted pyrazolone derivatives with DFT approach.

The synthesis of two pyrazolone derivative compounds, PYR-I(4-Acetyl-1-(4-chlorophenyl)-3-isopropyl-1H-pyrazol-5(4H)-one) and PYR-II1-(4-Chlorophenyl))-3-isopropyl-5-oxo-4,5-5-dihydro-1H-pyrazole-4-carbaldehyde, their characterization by FT-IR, NMR, UV-Vis and GC-MS techniques, and the evaluation of the keto-enol tautomerization process of the structures along with the DFT approach and spectral data were reported in this paper. Spectral findings indicated that PYR-I was stable at the keto state. The IR spectrum recorded in solid form showed that the PYR-II structure was stable in the enol state, while the NMR spectrum in the solution medium showed that it was stable in the keto state. DFT-based analyses were realized with the B3LYP hybrid functional and the 6-311++G(d,p) basis set. The modelled keto, transition and enol state molecular geometries of structures were optimized in the gas phase and different solvent media and the total energy and dipole moment values were investigated at the specified theoretical level. The possible keto-enol tautomerism mechanism of the structures was evaluated through some thermodynamic parameters such as the difference in free Gibbs energy (ΔG), enthalpy (ΔH), entropy (ΔS), and predictive tautomeric equilibrium constants (Keq), acidity constants (pKa) and percentages of tautomers at 298.15 K and 1 atm pressure. The results of these analyses based on the DFT approach indicated that the keto-enol tautomer equilibrium heavily favours the keto form for PYR-I and the enol form for PYR-II in all cases. Moreover, natural bond orbital (NBO) analysis was performed for the tautomers, and the chemical reactivity profiles of the most stable tautomers were examined with the values of frontier molecular orbital energy and some reactivity descriptors.

Statistical significance, clinical importance and effect sizes: Enhancing understanding of a study's results.

BACKGROUND: The proper interpretation of a study's results requires both excellent understanding of good methodological practices and deep knowledge of prior results, aided by the availability of effect sizes. METHODS: This review takes the form of an expository essay exploring the complex and nuanced relationships among statistical significance, clinical importance, and effect sizes. RESULTS: Careful attention to study design and methodology will increase the likelihood of obtaining statistical significance and may enhance the ability of investigators/readers to accurately interpret results. Measures of effect size show how well the variables used in a study account for/explain the variability in the data. Studies reporting strong effects may have greater practical value/utility than studies reporting weak effects. Effect sizes need to be interpreted in context. Verbal summary characterizations of effect sizes (e.g., "weak", "strong") are fundamentally flawed and can lead to inappropriate characterization of results. Common language effect size (CLES) indicators are a relatively new approach to effect sizes that may offer a more accessible interpretation of results that can benefit providers, patients, and the public at large. CONCLUSIONS: It is important to convey research findings in ways that are clear to both the research community and to the public. At a minimum, this requires inclusion of standard effect size data in research reports. Proper selection of measures and careful design of studies are foundational to the interpretation of a study's results. The ability to draw useful conclusions from a study is increased when investigators enhance the methodological quality of their work.

Coordination Engineering of Fe-centered Catalysts for Superior Li-S Battery Performance.

Iron-nitrogen functionalized graphene has emerged as a promising cathode host for rechargeable lithium-sulfur batteries (RLSBs) due to its affordability and enhanced battery performance. To optimize its catalytical efficiency, we propose a novel approach involving coordination engineering. Our investigation spans a plethora of catalysts with varied coordination environments, focusing on elements B, C, N and O. We revealed that Fe-C4 and Fe-B2C2-h are particularly effective for promoting Li2S oxidation, whereas Fe-N4 excels in catalyzing the sulfur reduction reaction (SRR). Importantly, our study identified specific descriptors - namely, the Integrated Crystal Orbital Hamilton Population (ICOHP) and the bond length between Fe and S in Li2S adsorbed state - as the most effective predictive descriptors for Li2S oxidation barriers. Meanwhile, Li2S adsorption energy emerges as a reliable descriptor for assessing the SRR barrier. These identified descriptors are expected to be instrumental in rapidly identifying promising cathode hosts across various metal-centered systems with diverse coordination environments. Our findings not only offer valuable insights into the role of coordination environment, but also present an effective path for rapidly identifying high performance catalysts for RLSBs, enabling the acceleration of advanced RLSBs development.

Characterization, DFT study and evaluation of antioxidant potentials of mycosporine-like amino acids (MAAs) in the cyanobacterium Anabaenopsis circularis HKAR-22.

The physiological parameters such as growth, Chl a content, and photosynthetic performance of the experimental cyanobacterium Anabaenopsis circularis HKAR-22 were estimated to evaluate the cumulative effects of photosynthetically active radiation (PAR) and ultraviolet (UV) radiation. Maximum induction of UV-screening molecules, MAAs, was observed under the treatment condition of PAR + UV-A + UV-B (PAB) radiations. UV/VIS absorption spectroscopy and HPLC-PDA detection primarily confirmed the presence of MAA-shinorine (SN) having absorption maxima (λmax) at 332.3 nm and retention time (RT) of 1.47 min. For further validation of the presence of SN, HRMS, FTIR and NMR were utilized. UV-stress elevated the in vivo ROS scavenging and in vitro enzymatic antioxidant capabilities. SN exhibited substantial and concentration-dependent antioxidant capabilities which was determined utilizing 2,2-diphenyl-1-picryl-hydrazyl (DPPH), 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonate (ABTS), ferric reducing power (FRAP) and superoxide radical scavenging assay (SRSA). The density functional theory (DFT) method using B3LYP energy model and 6-311G++(d,p) basis set was implied to perform the quantum chemical calculation to systematically investigate the antioxidant nature of SN. The principal pathways involved in the antioxidant reactions along with the basic molecular descriptors affecting the antioxidant potentials of a compound were also studied. The results favor the potential of SN as an active ingredient to be used in cosmeceutical formulations.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Revisiting nucleophilicity: an index for chemical reactivity from a CDFT approach.

CONTEXT: Understanding and predicting the nucleophilic reactivity are paramount in elucidating organic chemical reactions and designing new synthetic pathways. In this study, we propose a nucleophilicity index within the framework of Conceptual Density Functional Theory (CDFT). Through rigorous theoretical formulations, we introduce an original quantum reactivity descriptor that captures the nucleophilic propensity of molecules based on their electronic structure and chemical environment. Subsequently, this proposed index is applied to a series of nucleophiles (pyrrolidines derivatives), spanning a diverse range of chemical functionalities. Our computational assessments reveal insightful correlations between the predicted nucleophilicity index and experimental observations of nucleophilic behavior. Thereby, they offer a promising avenue for advancing the understanding of organic reactivity and guiding synthetic efforts. METHODS: Experimentally, Mayr's experimental parameters accounting for nucleophilicity were selected for the pyrrolidines. This study used DFT calculations at the B3LYP/Aug-cc-pVTZ level of theory using the Gaussian 16 program. Geometry optimization was thus performed, and the methodology employed for the computation of quantum reactivity descriptor is presented. Solvent effect was also taken into account using IEFPCM, and empirical dispersion correction (GD3) was employed.

Theoretical Insight into Psittacofulvins and Their Derivatives.

Psittacofulvins are polyenal dyes responsible for coloring parrot feathers and protecting them against photo-oxidation, harmful radicals, and bacterial degradation. To explain the unusual properties of these compounds, the thermodynamic and global chemical activity descriptors characterizing four natural and three synthetic psittacofulvins, as well as their hydroxyl, carboxyl and dialdehyde derivatives, were determined. To this aim, the DFT method at the B3LYP/QZVP theory level and the C-PCM solvation model were used. The calculations enabled the selection of the projected compounds for the greatest bioactivity and potential applicability as multifunctional ingredients in medicines, cosmetics, supplements, and food, in which they may play a triple role as preservative, radical scavenger, and coloring agent. The results obtained provide arguments for the identification of a fifth psittacofulvin within the parrot feather pigment, characterized by ten conjugated double bonds (docosadecaenal).

Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms.

Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as r and mean absolute error (MAE), and statistical analysis. At 45 ∘ C , logP predominantly influenced retention, akin to reversed-phase columns, while at 5 ∘ C , complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.

Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds.

Stereochemistry plays a fundamental role in pharmacology. Here, we systematically investigate the relationship between stereoisomerism and bioactivity on over 1 M compounds, finding that a very significant fraction (~ 40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then use the 3D representation of these molecules to train a collection of deep neural networks (Signaturizers3D) to generate bioactivity descriptors associated to small molecules, that capture their effects at increasing levels of biological complexity (i.e. from protein targets to clinical outcomes). Further, we assess the ability of the descriptors to distinguish between stereoisomers and to recapitulate their different target binding profiles. Overall, we show how these new stereochemically-aware descriptors provide an even more faithful description of complex small molecule bioactivity properties, capturing key differences in the activity of stereoisomers.Scientific contributionWe systematically assess the relationship between stereoisomerism and bioactivity on a large scale, focusing on compound-target binding events, and use our findings to train novel deep learning models to generate stereochemically-aware bioactivity signatures for any compound of interest.

Comparison of Vegetarian Sausages: Proximal Composition, Instrumental Texture, Rapid Descriptive Sensory Method and Overall Consumer Liking.

The aim of the present research was to determine if the developed ovo-vegetarian sausage (SO), which was made with 15% chickpea flour, 51% albumin and 34% soy protein concentrate, exhibited improved physicochemical and sensory characteristics compared to vegetarian sausages available on the local market (classic vegan sausage, SC; vegan fine herb sausage, SH; and quinoa sausage, SQ). According to the physicochemical results, the developed sample, SO, presented significant differences (p < 0.05) compared to the others, including higher protein content, lower pH and a higher a* value. Three types of sensory analyses were conducted-flash profile, overall liking and purchase intention (to determine consumers' willingness to purchase the product)-with the first involving 15 consumers and the second and third involving 60 participants each. Descriptors for each sample were determined using the vocabulary provided by consumers in the flash profile analysis. Descriptors for SO included 'elastic', 'smell of cooked corn', 'characteristic flavor', 'pasty', 'soft' and 'pastel color', contributing to its greater overall liking and purchase intention compared to the others. Through the hierarchical multiple factor analysis, a positive correlation was observed between the texture and sensory descriptors of the flash profile. Conversely, a correlation was found between the physicochemical characteristics (pH, aw, color) and overall liking and purchase intention.

Graph Neural Networks with Multi-Features for Predicting Cocrystals Using APIs and coformers Interactions.

Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals when reacting with coformers, as a potential and attractive route for drug substance development. However, screening and selecting suitable and appropriate coformers that may potentially react with APIs to successfully form cocrystals is a time-consuming, inefficient, costly, and labour intensive task. In this study, we implemented graph neural networks to predict the formation of cocrystals using our first created API coformers interactions graph dataset. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting, and artificial neural networks). All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60 and 95. 95% for GCN, GraphSAGE, and R-GCN respectively). Furthermore, R-GCN prevailed among the built graph-based models because of its capability to learn the topological structure of the graph from the additionally provided information (i.e., non-ionic and non-covalent interactions or link information) between APIs and coformers.

Applications of magnesium iodide structure via modified-polynomials.

A relatively recent approach in molecular graph theory for analyzing chemical networks and structures is called a modified polynomial. It emphasizes the characteristics of molecules through the use of a polynomial-based procedure and presents numerical descriptors in algebraic form. The Quantitative Structure-Property Relationship study makes use of Modified Polynomials (M-Polynomials) as a mathematical tool. M-Polynomials used to create connections between a material's various properties and its structural characteristics. In this study, we calculated several modified polynomials and gave a polynomial description of the magnesium iodide structure. Particularly, we computed first, second and modified Zagreb indices based M-polynomials. Randic index, and inverse Randic indices based M-polynomials are also computed in this work.

Interpretable Data-Driven Descriptors for Establishing the Structure-Activity Relationship of Metal-Organic Frameworks Toward Oxygen Evolution Reaction.

The development of readily accessible and interpretable descriptors is pivotal yet challenging in the rational design of metal-organic framework (MOF) catalysts. This study presents a straightforward and physically interpretable activity descriptor for the oxygen evolution reaction (OER), derived from a dataset of bimetallic Ni-based MOFs. Through an artificial-intelligence (AI) data-mining subgroup discovery (SGD) approach, a combination of the d-band center and number of missing electrons in eg states of Ni, as well as the first ionization energy and number of electrons in eg states of the substituents, is revealed as a gene of a superior OER catalyst. The found descriptor, obtained from the AI analysis of a dataset of MOFs containing 3-5d transition metals and 13 organic linkers, has been demonstrated to facilitate in-depth understanding of structure-activity relationship at the molecular orbital level. The descriptor is validated experimentally for 11 Ni-based MOFs. Combining SGD with physical insights and experimental verification, our work offers a highly efficient approach for screening MOF-based OER catalysts, simultaneously providing comprehensive understanding of the catalytic mechanism.

Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.

The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

Examining functional group-dependent effects on the ionization of lignin monomers using supercritical fluid chromatography/electrospray ionization mass spectrometry.

The chemical and biological conversion of biomass-derived lignin is a promising pathway for producing valuable low molecular weight aromatic chemicals, such as vanillin or guaiacol, known as lignin monomers (LMs). Various methods employing chromatography and electrospray ionization-mass spectrometry (ESI-MS) have been developed for LM analysis, but the impact of LM chemical properties on analytical performance remains unclear. This study systematically optimized ESI efficiency for 24 selected LMs, categorized by functionality. Fractional factorial designs were employed for each LM to assess ESI parameter effects on ionization efficiency using ultra-high-performance supercritical fluid chromatography/ESI-MS (UHPSFC/ESI-MS). Molecular descriptors were also investigated to explain variations in ESI parameter responses and chromatographic retention among the LMs. Structural differences among LMs led to complex optimal ESI settings. Notably, LMs with two methoxy groups benefited from higher gas and sheath gas temperatures, likely due to their lower log P and higher desolvation energy requirements. Similarly, vinyl acids and ketones showed advantages at elevated gas temperatures. The retention in UHPSFC using a diol stationary phase was correlated with the number of hydrogen bond donors. In summary, this study elucidates structural features influencing chromatographic retention and ESI efficiency in LMs. The findings can aid in developing analytical methods for specific technical lignins. However, the absence of an adequate number of LM standards limits the prediction of LM structures solely based on ESI performance data.

Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.

The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-consuming and costly, and machine learning (ML) methods can offer a viable solution to address these challenges. This study aimed at developing a ML model that incorporates chemical descriptors to predict the phytotoxicity of organic contaminants on rice. A dataset was compiled by gathering published experimental data on the phytotoxicity of 60 organic compounds, with a focus on morphological inhibition, photosynthesis perturbation, and oxidative stress. Four ML models (RF, SVM, GBM, ANN) were developed using chemical molecular descriptors (CMD) and the Molecular ACCess System (MACCS) keys. RF-MACCS model demonstrated the highest fitness, achieving an R2 value of 0.79 and an RMSE of 0.14. Feature importance analysis highlighted nAtom, HBA, logKow, and TPSA as the most influential CMDs in our model. Additionally, substructures containing oxygen atoms, carbonyl group and carbon chains with nitrogen and oxygen atoms were identified as significant factors associated with phytotoxicity. This data-driven study could aid in predicting the phytotoxicity of organic contaminants on crops and evaluating the potential risks of emerging contaminants in agroecosystems.

A BERT-based pretraining model for extracting molecular structural information from a SMILES sequence.

Among the various molecular properties and their combinations, it is a costly process to obtain the desired molecular properties through theory or experiment. Using machine learning to analyze molecular structure features and to predict molecular properties is a potentially efficient alternative for accelerating the prediction of molecular properties. In this study, we analyze molecular properties through the molecular structure from the perspective of machine learning. We use SMILES sequences as inputs to an artificial neural network in extracting molecular structural features and predicting molecular properties. A SMILES sequence comprises symbols representing molecular structures. To address the problem that a SMILES sequence is different from actual molecular structural data, we propose a pretraining model for a SMILES sequence based on the BERT model, which is widely used in natural language processing, such that the model learns to extract the molecular structural information contained in the SMILES sequence. In an experiment, we first pretrain the proposed model with 100,000 SMILES sequences and then use the pretrained model to predict molecular properties on 22 data sets and the odor characteristics of molecules (98 types of odor descriptor). The experimental results show that our proposed pretraining model effectively improves the performance of molecular property prediction SCIENTIFIC CONTRIBUTION: The 2-encoder pretraining is proposed by focusing on the lower dependency of symbols to the contextual environment in a SMILES than one in a natural language sentence and the corresponding of one compound to multiple SMILES sequences. The model pretrained with 2-encoder shows higher robustness in tasks of molecular properties prediction compared to BERT which is adept at natural language.

Events in context-The HED framework for the study of brain, experience and behavior.

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.

Guidelines for descriptor assignments for the solvation parameter model by separation techniques.

The solvation parameter model uses six compound descriptors to model equilibrium properties in biphasic systems formally defined as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L. The V descriptor can be assigned from structure and the E descriptor for compounds liquid at 20 °C can be calculated from its refractive index and characteristic volume. The E descriptor for compounds solid at 20 °C and the S, A, B, and L descriptors are assigned from experimental properties traditionally obtained by chromatographic, liquid-liquid partition, and solubility measurements. Here I report an efficient experimental design using the Solver method for the accurate assignment of descriptors for neutral compounds that simultaneously minimizes laboratory resources. This multi-technique approach requires 3 retention factor measurements in a 60 °C temperature range per compound on four columns by gas chromatography, 3 retention factor measurements in a 30 % (v/v) acetonitrile composition range per compound on two columns by reversed-phase liquid chromatography, and eight partition constant measurements by liquid-liquid partition in totally organic and aqueous biphasic systems for a total of 26 experimental measurements. The accuracy of the descriptor assignments was validated by comparison with the values in the Wayne State University (WSU) descriptor database taken as the best estimate of the true descriptor values. The E, S, A, B and L descriptors were assigned simultaneously by the Solver method using the above approach without significant bias and with an average absolute deviation (AAD) of 0.054, 0.018, 0.015, 0.013, and 0.040, respectively, compared with the WSU database values, corresponding to a relative absolute average deviation in percent (RAAD) of 7.2, 1.9, 3.6, 5.1, and 0.84 %, respectively, for 32 varied compounds. This streamlined approach represents a significant improvement on earlier single-technique approaches used as the starting point for the development of the multi-technique approach. For compounds of variable hydrogen-bond basicity modifications to the multi-technique approach were implemented while maintaining the same number of experimental measurements. Acceptable descriptor assignments for B/B° were obtained for compounds liquid at 20 °C for which the E descriptor was available by calculation. For solid compounds at 20 °C the E and B/B° descriptors are restricted to qualitative application where approximate values may be acceptable.

Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling.

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.