Explainability in Graph Neural Networks: A Taxonomic Survey

Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has achieved significant progress. In the area of graph data, graph neural networks (GNNs) and their explainability are experiencing rapid developments. However, there is neither a unified treatment of GNN explainability methods, nor a standard benchmark and testbed for evaluations. In this survey, we provide a unified and taxonomic view of current GNN explainability methods. Our unified and taxonomic treatments of this subject shed lights on the commonalities and differences of existing methods and set the stage for further methodological developments. To facilitate evaluations, we generate a set of benchmark graph datasets specifically for GNN explainability. We summarize current datasets and metrics for evaluating GNN explainability. Altogether, this work provides a unified methodological treatment of GNN explainability and a standardized testbed for evaluations

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for casual subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Folded Conformation, Cyclic Pentamer, Nanostructure, and PAD4 Binding Mode of YW3-56

The physical and chemical mechanisms of small molecules with pharmacological activity forming nanostructures are developing into a new field of nanomedicine. By using ROESY 2D NMR spectroscopy, tandem mass spectroscopy, transmission electron microscopy, and computer-assisted molecular modeling, this paper demonstrates the contribution of the folded conformation, the intra- and intermolecular π–π stacking, the intra- and intermolecular hydrogen bonds, and the receptor binding free energy of 6-dimethylaminonaph-2-yl-{<i>N</i>-<i>S</i>-[1-benzylcarba-moyl-4-(2-chloroacetamidobutyl)]-carboxamide (YW3-56) to the rapid formation of nanorings and the slow formation of nanocapsules. Thus we have developed a strategy that makes it possible to elucidate the physical and chemical mechanisms of bioactive small molecules forming nanostructures

Structure-Based Design of Tropane Derivatives as a Novel Series of CCR5 Antagonists with Broad-Spectrum Anti-HIV‑1 Activities and Improved Oral Bioavailability

Blocking the entry of an HIV-1 targeting CCR5 coreceptor has emerged as an attractive strategy to develop HIV therapeutics. Maraviroc is the only CCR5 antagonist approved by FDA; however, serious side effects limited its clinical use. Herein, 21 novel tropane derivatives (6–26) were designed and synthesized based on the CCR5-maraviroc complex structure. Among them, compounds 25 and 26 had comparable activity to maraviroc and presented more potent inhibitory activity against a series of HIV-1 strains. In addition, compound 26 exhibited synergistic or additive antiviral effects in combination with other antiretroviral agents. Compared to maraviroc, both 25 and 26 displayed higher Cmax and AUC0–∞ and improved oral bioavailability in SD rats. In addition, compounds 25 and 26 showed no significant CYP450 inhibition and showed a novel binding mode with CCR5 different from that of maraviroc-CCR5. In summary, compounds 25 and 26 are promising drug candidates for the treatment of HIV-1 infection

Structure-Based Design of Tropane Derivatives as a Novel Series of CCR5 Antagonists with Broad-Spectrum Anti-HIV‑1 Activities and Improved Oral Bioavailability

Blocking the entry of an HIV-1 targeting CCR5 coreceptor has emerged as an attractive strategy to develop HIV therapeutics. Maraviroc is the only CCR5 antagonist approved by FDA; however, serious side effects limited its clinical use. Herein, 21 novel tropane derivatives (6–26) were designed and synthesized based on the CCR5-maraviroc complex structure. Among them, compounds 25 and 26 had comparable activity to maraviroc and presented more potent inhibitory activity against a series of HIV-1 strains. In addition, compound 26 exhibited synergistic or additive antiviral effects in combination with other antiretroviral agents. Compared to maraviroc, both 25 and 26 displayed higher Cmax and AUC0–∞ and improved oral bioavailability in SD rats. In addition, compounds 25 and 26 showed no significant CYP450 inhibition and showed a novel binding mode with CCR5 different from that of maraviroc-CCR5. In summary, compounds 25 and 26 are promising drug candidates for the treatment of HIV-1 infection

Structure-Based Design of Tropane Derivatives as a Novel Series of CCR5 Antagonists with Broad-Spectrum Anti-HIV‑1 Activities and Improved Oral Bioavailability

Blocking the entry of an HIV-1 targeting CCR5 coreceptor has emerged as an attractive strategy to develop HIV therapeutics. Maraviroc is the only CCR5 antagonist approved by FDA; however, serious side effects limited its clinical use. Herein, 21 novel tropane derivatives (6–26) were designed and synthesized based on the CCR5-maraviroc complex structure. Among them, compounds 25 and 26 had comparable activity to maraviroc and presented more potent inhibitory activity against a series of HIV-1 strains. In addition, compound 26 exhibited synergistic or additive antiviral effects in combination with other antiretroviral agents. Compared to maraviroc, both 25 and 26 displayed higher Cmax and AUC0–∞ and improved oral bioavailability in SD rats. In addition, compounds 25 and 26 showed no significant CYP450 inhibition and showed a novel binding mode with CCR5 different from that of maraviroc-CCR5. In summary, compounds 25 and 26 are promising drug candidates for the treatment of HIV-1 infection