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Updated algorithms for predicting the volumes of systemic circulation (V1), along with absorption rate constants and hepatic intrinsic clearances, as input parameters for physiologically based pharmacokinetic (PBPK) models were established to improve the accuracy of estimated plasma and tissue concentrations of 323 chemicals after virtual oral administrations in rats. Using ridge regression with an enlarged set of chemical descriptors (up to 99), the estimated input V1 values resulted in an improved correlation coefficient (from 246 compounds) with the traditionally determined values. The PBPK model input parameters for rats of diverse compounds can be precisely estimated by increasing the number of descriptors.

The aim of scaffold hopping (SH) is to find compounds consisting of different scaffolds from those in already known active compounds, giving an opportunity for unexplored regions of chemical space. We previously demonstrated the usefulness of pharmacophore graphs (PhGs) for this purpose through proof-of-concept virtual screening experiments. PhGs consist of nodes and edges corresponding to pharmacophoric features (PFs) and their topological distances. Although PhGs were effective in SH, they are hard to interpret as they are complete graphs. Herein, we introduce an intuitive representation of a molecule, termed as sparse pharmacophore graphs (SPhG) by keeping the topological distances among PFs as much as possible while reducing the number of edges in the graphs. Several benchmark calculations quantitatively confirmed the sparseness of the graphs and the preservation of topological distances among pharmacophoric points. As proof-of-concept applications, virtual screening (VS) trials for SH were conducted using active and inactive compounds from ChEMBL and PubChem databases for three biological targets: thrombin, tyrosine kinase ABL1, and κ-opioid receptor. The performances of VS were comparable with using fully connected PhGs. Furthermore, highly ranked SPhGs were interpretable for the three biological targets, in particular for thrombin, for which selected SPhGs were in agreement with the structure-based interpretation.

In ligand-based drug design, quantitative structure-activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC50). Experimental IC50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

Designing polymers experimentally is a time-consuming task. Quantitative structure-property relationship analysis can help speed the development of new polymers. The authors hypothesized the ideal ...

Recently developed computational models can estimate plasma, hepatic, and renal concentrations of industrial chemicals in rats. Typically, the input parameter values (i.e., the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance) for simplified physiologically based pharmacokinetic (PBPK) model systems are calculated to give the best fit to measured or reported in vivo blood substance concentration values in animals. The purpose of the present study was to estimate in silico these three input pharmacokinetic parameters using a machine learning algorithm applied to a broad range of chemical properties obtained from several cheminformatics software tools. These in silico estimated parameters were then incorporated into PBPK models for predicting internal exposures in rats. Following this approach, simplified PBPK models were set up for 246 drugs, food components, and industrial chemicals with a broad range of chemical structures. We had previously generated PBPK models for 158 of these substances, whereas 88 for which concentration series data were available in the literature were newly modeled. The values for the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance could be generated in silico by equations containing between 14 and 26 physicochemical properties. After virtual oral dosing, the output concentration values of the 246 compounds in plasma, liver, and kidney from rat PBPK models using traditionally determined and in silico estimated input parameters were well correlated (r ≥ 0.83). In summary, by using PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments with in silico-derived input parameters, the forward dosimetry of new chemicals could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of hematotoxic, hepatotoxic, or nephrotoxic potential as a part of risk assessment.

Soft sensors have been widely employed in industrial processes to estimate process variables that are difficult to measure in real time. Taking into account process dynamics in soft sensor models is important not only for high predictability, but also for the interpretation of the models. One simple way of introducing dynamics is to use process variables X with time-delays. In this study, we propose a strategy of building soft sensor models for selecting appropriate variables X with time-delays, by incorporating ensemble learning into genetic algorithm-based process variables and dynamics selection (GAVDS). The construction of soft sensor models using simulation data sets and publicly available process operation data sets showed that the ensemble GAVDS method was superior to GAVDS in terms of model predictability. Furthermore, it appeared to select reasonable time delay regions for process variables X.

Recently developed high-throughput in vitro assays in combination with computational models could provide alternatives to animal testing. The purpose of the present study was to model the plasma, hepatic, and renal pharmacokinetics of approximately 150 structurally varied types of drugs, food components, and industrial chemicals after virtual external oral dosing in rats and to determine the relationship between the simulated internal concentrations in tissue/plasma and their lowest-observed-effect levels. The model parameters were based on rat plasma data from the literature and empirically determined pharmacokinetics measured after oral administrations to rats carried out to evaluate hepatotoxic or nephrotic potentials. To ensure that the analyzed substances exhibited a broad diversity of chemical structures, their structure-based location in the chemical space underwent projection onto a two-dimensional plane, as reported previously, using generative topographic mapping. A high-throughput in silico one-compartment model and a physiologically based pharmacokinetic (PBPK) model consisting of chemical receptor (gut), metabolizing (liver), central (main), and excreting (kidney) compartments were developed in parallel. For 159 disparate chemicals, the maximum plasma concentrations and the areas under the concentration-time curves obtained by one-compartment models and modified simple PBPK models were closely correlated. However, there were differences between the PBPK modeled and empirically obtained hepatic/renal concentrations and plasma maximal concentrations/areas under the concentration-time curves of the 159 chemicals. For a few compounds, the lowest-observed-effect levels were available for hepatotoxicity and nephrotoxicity in the Hazard Evaluation Support System Integrated Platform in Japan. The areas under the renal or hepatic concentration-time curves estimated using PBPK modeling were inversely associated with these lowest-observed-effect levels. Using PBPK forward dosimetry could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of nephrotoxic or hepatotoxic potential as a part of the risk assessment.

Graphical abstract, Highlights • Permeability values of 90 industry chemicals were measured by a Caco-2 system. • A multivariate prediction equation for permeability of chemicals was proposed. • Chemical permeability coefficients were inversely associated with hepatic NOELs., Apparent permeability coefficients (Papp) across a human intestinal epithelial Caco-2 cell monolayer were measured for a range of industrial/drug chemicals. A predictive equation for determining in vitro Papp values of fifty-six substances was set up using multivariate regression analysis based on in silico-estimated physicochemical properties (molecular weights and water distribution coefficients for apical and basal pH environments) (r = 0.77, p

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

Quantitative structure-property relationship models are useful in efficiently searching for molecules with desired properties in drug discovery and materials development. In recent years, many such models based on graph neural networks, showing good prediction performance, have been reported. Training graph neural networks generally require many samples, but by using a training method for a small dataset, it is possible to extract features that enable successful prediction. Herein, we design a method of augmenting graph data. In this method, random perturbations are added with a certain probability to some vertex features during message passing. We verify the proposed method's effectiveness in regression and classification tasks. It is confirmed that the proposed method is effective when the perturbation is added immediately before the readout of the graph neural network, and the effect of the data augmentation is most evident for small datasets of approximately 1000 samples.

Updated algorithms for predicting the volumes of systemic circulation (

Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers’ decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner.

For medicines, the apparent membrane permeability coefficients (Papp) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro Papp values of 29 industrial chemicals were found to have an inverse association with their reported no-observed effect levels for hepatotoxicity in rats. In the current study, we expanded our influx permeability predictions for the 90 previously investigated chemicals to both influx and efflux permeability predictions for 207 diverse primary compounds, along with those for 23 secondary compounds. Trivariate linear regression analysis found that the observed influx and efflux logPapp values determined by in vitro experiments significantly correlated with molecular weights and the octanol–water distribution coefficients at apical and basal pH levels (pH 6.0 and 7.4, respectively) (apical to basal, r = 0.76, n = 198; and basal to apical, r = 0.77, n = 202); the distribution coefficients were estimated in silico. Further, prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM) to estimate influx and efflux logPapp values that incorporated 17 and 19 in silico chemical descriptors (r = 0.83–0.84, p