The optimal design of compounds through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance. This paper highlights key trends, challenges, and opportunities underpinning the Computer-Aided Molecular Design (CAMD) problems. A brief review of knowledge-driven property estimation methods and solution techniques, as well as corresponding CAMD tools and applications, are first presented. In view of the computational challenges plaguing knowledge-based methods and techniques, we survey the current state-of-the-art applications of deep learning to molecular design as a fertile approach towards overcoming computational limitations and navigating uncharted territories of the chemical space. The main focus of the survey is given to deep generative modeling of molecules under various deep learning architectures and different molecular representations. Further, the importance of benchmarking and empirical rigor in building deep learning models is spotlighted. The review article also presents a detailed discussion of the current perspectives and challenges of knowledge-based and data-driven CAMD and identif
In today's landscape, hardware development teams face increasing demands for better quality products, greater innovation, and shorter manufacturing lead times. Despite the need for more efficient and effective processes, hardware designers continue to struggle with a lack of awareness of design changes and other collaborators' actions, a persistent issue in decades of CSCW research. One significant and unaddressed challenge is understanding and managing dependencies between 3D CAD (computer-aided design) models, especially when products can contain thousands of interconnected components. In this two-phase formative study, we explore designers' pain points of CAD dependency management through a thematic analysis of 100 online forum discussions and semi-structured interviews with 10 designers. We identify nine key challenges related to the traceability, navigation, and consistency of CAD dependencies, that harm the effective coordination of hardware development teams. To address these challenges, we propose design goals and necessary features to enhance hardware designers' awareness and management of dependencies, ultimately with the goal of improving collaborative workflows.
Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making better informed decisions, and create designs that are both innovative and practical. The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities among diverse CAD models, the proposition of alternative designs and enhancements, and even the generation of novel design alternatives. This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories, including similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds, and single/multi-view images. Additionally, it provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain. The final discussion delves into th
Geometric modelling has been a crucial component of the design process ever since the introduction of the first Computer-Aided Design (CAD) systems. Additive Manufacturing (AM) pushes design freedom to previously unachievable limits. AM allows the manufacturing of lattice structures which are otherwise close to impossible to be manufactured conventionally. Yet, the geometric modelling of heterogeneous lattice structures is still greatly limited. Thus, the AM industry is now in a situation where the manufacturing capabilities exceed the geometric modelling capabilities. While there have been advancements in the modelling of heterogeneous lattice structures, the review of relevant literature revealed critical limitations of the existing approaches. These limitations include their inability to model non-linear variation of geometric parameters, as well as the limited amount of controllable geometric parameters. This work presents a novel geometric modelling methodology based on function representation as an attempt to bridge this gap. The proposed approach avoids the manual definition of geometric parameters and provides a method to control them with mathematical functions instead. A
Rankings of scholarly journals based on citation data are often met with skepticism by the scientific community. Part of the skepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of authors. This paper focuses on analysis of the table of cross-citations among a selection of Statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care in order to avoid potential over-interpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's Research Assessment Exercise shows strong correlation at aggregate level between assessed research quality and journal citation `export scores' within the discipline of Statistics.
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data structures such as molecular structures efficiently and are thus highly relevant to computer-aided molecular design (CAMD). We propose a bilinear formulation for ReLU Graph Convolutional Neural Networks and a MILP formulation for ReLU GraphSAGE models. These formulations enable solving optimisation problems with trained GNNs embedded to global optimality. We apply our optimization approach to an illustrative CAMD case study where the formulations of the trained GNNs are used to design molecules with optimal boiling points.
Numerous biological functions-such as enzymatic catalysis, the immune response system, and the DNA-protein regulatory network-rely on the ability of molecules to specifically recognize target molecules within a large pool of similar competitors in a noisy biochemical environment. Using the basic framework of signal detection theory, we treat the molecular recognition process as a signal detection problem and examine its overall performance. Thus, we evaluate the optimal properties of a molecular recognizer in the presence of competition and noise. Our analysis reveals that the optimal design undergoes a "phase transition" as the structural properties of the molecules and interaction energies between them vary. In one phase, the recognizer should be complementary in structure to its target (like a lock and a key), while in the other, conformational changes upon binding, which often accompany molecular recognition, enhance recognition quality. Using this framework, the abundance of conformational changes may be explained as a result of increasing the fitness of the recognizer. Furthermore, this analysis may be used in future design of artificial signal processing devices based on bio
This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.
Polypills are single oral dosage forms that combine multiple active pharmaceutical ingredients and excipients, enabling fixed-dose combination therapies, coordinated multi-phase release, and precise customization of patient-specific treatment protocols. Recent advances in additive manufacturing facilitate the physical realization of multi-material excipients, offering superior customization of target release profiles. However, polypill formulations remain tuned by ad hoc parameter sweeps. The current design workflows are ill-suited for the systematic exploration of the high-dimensional space of shapes, compositions, and release behaviors. We present PILL-CoDe, a polypill co-design framework that simultaneously optimizes tablet geometry and excipient distribution to match prescribed drug-release kinetics. The framework couples a supershape parametrization of the pill geometry with a coordinate-based neural network representation of the excipient distribution, and governs dissolution through a coupled system of modified Allen-Cahn and Fickian diffusion equations. Implemented in JAX, the entire pipeline is end-to-end differentiable, with automatic differentiation providing exact sensi
Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Since the Scopus database contains a larger number of journals and covers also the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is due to (i) the larger number of journals covered by Scopus and (ii) the historical record of citations older than ten years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.
Security risk assessment is essential in establishing the trustworthiness and reliability of modern systems. While various security risk assessment approaches exist, prevalent applications are "pen and paper" implementations that -- even if performed digitally using computers -- remain prone to authoring mistakes and inconsistencies. Computer-aided design approaches can transform security risk assessments into more rigorous and sustainable efforts. This is of value to both industrial practitioners and researchers, who practice security risk assessments to reflect on systems' designs and to contribute to the discipline's state-of-the-art. In this article, we report the application of a model-based security design tool to reproduce a previously reported security assessment. The main contributions are: 1) an independent attempt to reproduce a refereed article describing a real security risk assessment of a system; 2) comparison of a new computer-aided application with a previous non-computer-aided application, based on a published, real-world case study; 3) a showcase for the potential advantages -- for both practitioners and researchers -- of using computer-aided design approaches to
A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called "Web-of-Science Subject Categories" (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals = 11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal-journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem
An example of a nonspecific molecular bond is the affinity of any positive charge for any negative charge (like-unlike), or of nonpolar material for itself when in aqueous solution (like-like). This contrasts specific bonds such as the affinity of the DNA base A for T, but not for C, G, or another A. Recent experimental breakthroughs in DNA nanotechnology demonstrate that a particular nonspecific like-like bond ("blunt-end DNA stacking" that occurs between the ends of any pair of DNA double-helices) can be used to create specific "macrobonds" by careful geometric arrangement of many nonspecific blunt ends, motivating the need for sets of macrobonds that are orthogonal: two macrobonds not intended to bind should have relatively low binding strength, even when misaligned. To address this need, we introduce geometric orthogonal codes that abstractly model the engineered DNA macrobonds as two-dimensional binary codewords. While motivated by completely different applications, geometric orthogonal codes share similar features to the optical orthogonal codes studied by Chung, Salehi, and Wei. The main technical difference is the importance of 2D geometry in defining codeword orthogonality
Microstructures, characterized by intricate structures at the microscopic scale, hold the promise of important disruptions in the field of mechanical engineering due to the superior mechanical properties they offer. One fundamental technique of microstructure design and manufacturing is geometric modeling, which generates the 3D computer models required to run high-level procedures such as simulation, optimization, and process planning. There is, however, a lack of comprehensive discussions on this body of knowledge. The goal of this paper is to compile existing microstructure modeling methods and clarify the challenges, progress, and limitations of current research. It also concludes with future research directions that may improve and/or complement current methods, such as compressive and generative microstructure representations. By doing so, the paper sheds light on what has already been made possible for microstructure modeling, what developments can be expected in the near future, and which topics remain problematic.
Using "Analyze Results" at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy." Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.'s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal lit
A review of the mechanisms of speciation is performed. The mechanisms of the evolution of species, taking into account the feedback of the state of the environment and mechanisms of the emergence of complexity, are considered. It is shown that these mechanisms, at the molecular level, cannot work steadily in terms of classical mechanics. Quantum mechanisms of changes in the genome, based on the long-range interaction potential between biologically important molecules, are proposed as one of possible explanation. Different variants of interactions of the organism and environment based on molecular recognition and leading to new species origins are considered. Experiments to verify the model are proposed. This bio-physical study is completed by the general operational model of based on quantum information theory. The latter is applied to model epigenetic evolution.
Climate change and resource depletion demand a shift from the dominant linear "take-make-use-dispose" paradigm of construction toward circular, low-waste practices. Material reuse offers a promising pathway by reducing raw material extraction, mitigating waste, and extending the service lifespan of carbon-sequestering materials such as timber. Realizing this potential, however, requires addressing technical and logistical challenges across both design and construction for accommodating heterogeneous, reclaimed material inventories. This paper presents an integrated framework that couples data-driven computational design with feedback-driven adaptive human-robot collaborative (co-robotic) fabrication and assembly to enable the realization of nonstandard structures made from reclaimed timber of varying length and geometries, supplemented with new off-the-shelf timber when necessary. The framework is validated through Timbrelyn, a built case-study installation that demonstrates how timber reuse can inform and enhance architectural expression. This work contributes to the development of integrated design-to-fabrication workflows that advance adaptive, feedback-driven methods to handle
Design requirements for moving parts in mechanical assemblies are typically specified in terms of interactions with other parts. Some are purely kinematic (e.g., pairwise collision avoidance) while others depend on physics and material properties (e.g., deformation under loads). Kinematic design methods and physics-based shape/topology optimization (SO/TO) deal separately with these requirements. They rarely talk to each other as the former uses set algebra and group theory while the latter requires discretizing and solving differential equations. Hence, optimizing a moving part based on physics typically relies on either neglecting or pruning kinematic constraints in advance, e.g., by restricting the design domain to a collision-free space using an unsweep operation. In this paper, we show that TO can be used to co-design two or more parts in relative motion to simultaneously satisfy physics-based criteria and collision avoidance. We restrict our attention to maximizing linear-elastic stiffness while penalizing collision measures aggregated in time. We couple the TO loops for two parts in relative motion so that the evolution of each part's shape is accounted for when penalizing c
Publication patterns of 79 forest scientists awarded major international forestry prizes during 1990-2010 were compared with the journal classification and ranking promoted as part of the 'Excellence in Research for Australia' (ERA) by the Australian Research Council. The data revealed that these scientists exhibited an elite publication performance during the decade before and two decades following their first major award. An analysis of their 1703 articles in 431 journals revealed substantial differences between the journal choices of these elite scientists and the ERA classification and ranking of journals. Implications from these findings are that additional cross-classifications should be added for many journals, and there should be an adjustment to the ranking of several journals relevant to the ERA Field of Research classified as 0705 Forestry Sciences.
Objectives: To assess evaluative methodologies for comparative measurements of test sensitivity in clinical mammographic screening trials of computer-aided detection (CAD) technologies. Materials and Methods: This meta-analysis was performed by analytically reviewing the relevant literature on the clinical application of computer-aided detection (CAD) technologies as part of a breast cancer screening program based on x-ray mammography. Each clinical study's method for measuring the CAD system's improvement in test sensitivity is examined in this meta-analysis. The impact of the chosen sensitivity measurement on the study's conclusions are analyzed. Results: This meta-analysis demonstrates that some studies have inappropriately compared sensitivity measurements between control groups and CAD enabled groups. The inappropriate comparison of control groups and CAD enabled groups can lead to an underestimation of the benefits of the clinical application of computer-aided detection technologies. Conclusions: The potential for the sensitivity measurement issues raised in this meta-analysis to alter the conclusions of multiple existing large clinical studies is discussed. Two large scale s