Journal of Chemical Information and Modeling 最新文献

The third Critical Assessment of Computational Hit-finding Experiments (CACHE) challenged computational teams to identify chemically novel ligands targeting the macrodomain 1 of SARS-CoV-2 Nsp3, a promising coronavirus drug target. Twenty-three groups deployed diverse design strategies to collectively select 1739 ligand candidates. While over 85% of the designed molecules were chemically novel, the best experimentally confirmed hits were structurally similar to previously published compounds. Confirming a trend observed in CACHE #1 and #2, two of the best-performing workflows used compounds selected by physics-based computational screening methods to train machine learning models able to rapidly screen large chemical libraries, while four others used exclusively physics-based approaches. Three pharmacophore searches and one fragment growing strategy were also part of the seven winning workflows. While active molecules discovered by CACHE #3 participants largely mimicked the adenine ring of the endogenous substrate, ADP-ribose, preserving the canonical chemotype commonly observed in previously reported Nsp3-Mac1 ligands, they still provide novel structure–activity relationship insights that may inform the development of future antivirals. Collectively, these results show that multiple molecular design strategies can efficiently converge on similar potent molecules.

The utilization of predictive tools has become increasingly prevalent in the development of biopharmaceuticals, reducing the time and cost of research. However, most methods for computational antibody design are hampered by their reliance on scarcely available antibody structures, potential for immunogenic modifications, and a restricted exploration of the paratope’s potential chemical and conformational space. We propose Ab-SELDON, a modular and easily customizable antibody design pipeline capable of iteratively optimizing an antibody–antigen (Ab–Ag) interaction in five different modification steps, including CDR and framework grafting, and mutagenesis. The optimization process is guided by diversity data collected from millions of publicly available human antibody sequences. This approach enhanced the exploration of the chemical and conformational space of the paratope during computational tests involving the optimization of an anti-HER2 antibody. Optimization of another antibody against Gal-3BP stabilized the Ab-Ag interaction in molecular dynamics simulations at lower runtime than alternative pipelines. Tests with SKEMPI’s Ab-Ag mutations also demonstrated the pipeline’s ability to correctly identify the effect of the majority of mutations, especially multipoint and those that increased binding affinity. This freely available pipeline presents a new approach for computationally efficient and automated in silico antibody design, thereby facilitating the development of new biopharmaceuticals.

Drug-target interactions (DTIs) are the basis of the therapeutic effect of drugs, whose accurate prediction helps reduce the cost and time of experimental screening in drug development process. Present methods for DTIs prediction often focus on the study of molecular topological structure, which weakens spatial information such as the relative position of atoms and bond angle, and fail to effectively integrate molecular information with association network information. To address this issue, we propose a novel Geometry-enhanced Multiscale Joint Representation Learning method for drug-target interaction prediction (GMJRL). GMJRL not only considers the global information in the drug-target network from the macro-scale, but also extracts the geometric structure information on the drug and the target from the microscale, including the bond angle information on the drug and the atomic coordinate information on the target. To effectively fuse different scale representations, we develop a joint representation learning method with self-attention, which can capture correlations within the same scale and consider the interscale relationships, thus achieving effective fusion of the macro-scale and microscale representations. Finally, this study introduces a negative sampling algorithm to select reliable negative samples from unlabeled drug-target pairs. Extensive experiments validate that GMJRL yields promising outcomes in predicting drug-target interactions.

Phase separation in bilayers composed of a few lipid species is widely used as a model for exploring the lateral heterogeneity of complex cell membranes. Molecular dynamics (MD) simulations offer atomistic insights into coexisting lipid phases. But identifying these phases from trajectories remains challenging. Here, we present an unsupervised method for lipid phase recognition in phase-separated bilayers. In this method, the membrane plane is first discretized into pixels. For each pixel, the local lipid packing degree, which is defined as the atomic density within that pixel, is calculated and assigned to the corresponding pixel. A threshold is then determined by fitting a two-component Gaussian mixture model (GMM) to the distribution of lipid packing degree, enabling phase state assignment to pixels and subsequent mapping back to lipids. Our method is applicable to different systems, regardless of their compositions or temperatures, thus minimizing potential artifacts. Tests on bilayers with diverse lipid compositions and temperatures show that our method outperforms the commonly used hidden Markov model (HMM) in both accuracy and robustness. Notably, in this method, phase recognition relies solely on bilayer-intrinsic properties (lipid packing degree), without requiring temporal information, labeled data, or assumptions about the local lipid environment. This makes our method broadly applicable to various tasks, including characterizing the phase transformation process before the system reaches equilibration and identifying coexisting phases in protein-containing bilayers. In summary, we provide a robust and accurate framework for identifying coexisting phases in bilayers and tracking their dynamic transitions in simulations.

Bitterness, alongside sour, sweet, umami, and salty tastes, constitutes one of the five basic tastes and serves as a key dimension in shaping food flavor profiles. Food protein processing readily generates bitter peptides, whose intense bitterness often leads to consumer rejection, yet these peptides frequently carry beneficial bioactivities, necessitating a trade-off between flavor and functionality. This necessitates the quantitative assessment of bitterness intensity in the early stages of product development. However, experimental assays relying on sensory evaluation and electronic tongue instruments are complex, costly, and limited in throughput, constraining the systematic identification of bitter peptides and process optimization. Here, we present BIPE (Bitterness Intensity Prediction Engine), an end-to-end regression model that integrates ESM3 protein language model representations with a multilayer perceptron readout, performing regression of bitterness thresholds in log space to directly assess bitterness intensity from sequence alone. BIPE achieves R² = 0.9050 under 10-fold cross-validation and R² = 0.9449 on an independent test set. BIPE accurately reproduces trends in both electronic tongue readouts and human sensory scores, demonstrating a consistent external validity across assays. Besides, BIPE accurately differentiates the bitterness intensities of soybean protein hydrolysates produced by multiple commercial proteases. Finally, systematic scanning of the complete pentapeptide sequence space by BIPE further reveals amino acid compositional patterns associated with bitterness, providing mechanistic insights. By advancing from classification to quantitative regression, BIPE enables rational design of low-bitterness peptides, supports flavor engineering and process optimization, and establishes a reusable baseline for taste modeling.

In this study, we introduce a sensitivity analysis methodology for stochastic systems in chemistry, where dynamics are often governed by random processes. Our approach is based on gradient estimation via finite differences, averaging simulation outcomes, and analyzing variability under intrinsic noise. We characterize gradient uncertainty as an angular range within which all plausible gradient directions are expected to lie. A key feature of our approach is that this uncertainty measure adaptively guides the number of simulations performed for each nominal-perturbation pair of points in order to minimize unnecessary computations while maintaining robustness. Systematically exploring a range of parameter values across the parameter space, rather than focusing on a single value, allows us to identify not only sensitive parameters but also regions of parameter space associated with different levels of sensitivity. These results are visualized through vector field plots to offer an intuitive representation of local sensitivity across parameter space. Additionally, global sensitivity coefficients over sampled points in the parameter space are computed to capture overall trends. Flexibility regarding the choice of output observable measures is another key feature of our method: while traditional sensitivity analyses often focus on species concentrations, our framework allows for the definition of a large range of problem-specific observables. This makes it broadly applicable in diverse chemical and biochemical scenarios. We demonstrate our approach on two systems: classical Michaelis–Menten kinetics and a rule-based model of the formose reaction, using the cheminformatics software MØD for Gillespie-based stochastic simulations.