IADE: a system for intelligent automatic design of bioisosteric analogs

This paper introduces a new approach named Design for Patentability (DFP) and presents the preliminary formulation of a formal methodology to attempt consideration of patentability aspects during the early stages of design including conceptual design and initial implementation of detailed design and manufacturing. Design for Automation (DFAM) approach formulated earlier by the first author based on Axiomatic Design Theory originated by Suh et. al. at MIT is adapted, suitably modified and customized for inclusion of patentability aspects such as anticipation, functionality, utility, and obviousness. Highlighting the complexity in incorporation of legal aspects in an engineering methodology, the paper presents the possibilities of improving the patentability of a design by a systematic and considered approach. The proposed methodology introduces a Patentability Evaluation phase in-between the Product Design, Process Design and Automation System Design phases of DFAM. The paper reviews mapping of parameters between different domains, namely, Functional Requirements Domain, Design Parameters Domain, Process Requirements Domain, and Design Automation Parameters Domain encompassed in the DFAM methodology and includes Patentability Parameters Domain in parallel to the last three domains to enable possible consideration of patentability aspects during Product Design, Process Design, and Automation System Design. Further, the paper briefly discusses the relevance of the Information Axiom of the Axiomatic Design Theory in the context of preparation of preliminary drafts of invention disclosure and potential claims for perusal by patent agents or attorneys. The approach reported in the paper is expected to have broad applications in the growing field of innovation based entrepreneurship in which design for patentability is an essential requirement for success of a business venture.

One of the important aspects in the design of automation system is to establish some automatic transformation method from the specification by high‐level language, which is suitable for designer to the description at the circuit level. This paper formulates the circuit design as the transformation, from the recursion equation, which is a high‐level description, to the functional network FN, which is a circuit model based on the data‐flow operation. The purpose of this paper is to clarify the transformation rules for the automatic circuit design based on the theorem‐proving techniques. First, the recursion equation is formulated as the specification language, and the circuit description language for FN is designed. The theorem‐proving system for the automatic design of the circuit can be formulated as a logic system in which the axioms are the predicate expressions for the elements, and the inference rules are the transformation rules among elements. The transformation rules from the recursion equation to the circuit representation are constructed using the concept of schema corresponding to the type of the recursion equation. Since the transformation includes heuristic aspects, some transformation techniques from the schema to the circuit description (called schema transformation) as well as the decomposition‐synthesis rules in the circuit representation, are presented. A logic system based on the obtained result is presented, indicating that a theorem‐proving system for the automatic circuit design can be constructed.

Computer-aided design (CAD) tools throughout a design automation (DA) system, from low level physical design to high level resource allocation and scheduling tools, require information about the fabrication technology in order to make design decisions. Technology dependent information is maintained in technology files. As fabrication technologies evolve, these files must be updated to reflect the capabilities of the current fabrication process technology. Currently, the task of updating technology files is performed manually and often prone to error and inconsistencies. A system for the automatic updating of technology dependent information in a design automation system is presented. The technology information required by the CAD tools throughout a design automation system are consistent since they are all derived from a common source. >

This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

In future smart cities, automatic landscape design can be viewed as a promising intelligent application to reduce the reliance on expert labors. As it is a kind of visual sensing activity, it is expected to develop a robust interaction platform with strong ability of visual information fusion. To deal with this issue, this paper integrates vision computing, and designs an automatic landscape design system in smart cities. The whole design framework can be attributed as three aspects of works: function analysis, structure design and implementation. Among, the visual information processing runs through the three aspects. Then, the generation process of landscape design is simulated in detail via a systematic case study. To prove the significance of visual information processing in our proposal, this article uses a model analysis method to compare the effects of traditional data processing technology and visual data processing technology. The analysis results show that vision computing technology provides technical support for landscape design. We also carry out some performance testing towards the designed automatic landscape design system, and evaluation results are demonstrated via visualization format. The designed automatic system is a proper prototype that can be developed to realistic engineering systems by some following completion.

Analog and mixed-signal integrated circuit (AMS-IC) designs remain a critical yet challenging aspect within electronic design automation (EDA), primarily due to the inherent complexity, nonlinear behavior, and increasing variability associated with advanced semiconductor technologies. Traditional manual and intuition-driven methodologies for AMS-ICs design, which rely heavily on iterative simulation loops and extensive designer experience, face significant limitations concerning efficiency, scalability, and reproducibility. Recently, machine learning (ML) techniques have emerged as powerful tools to address these challenges, offering significant enhancements in modeling, abstraction, optimization, and automation capabilities for AMS-ICs. This review systematically examines recent advancements in ML-driven methodologies applied to analog circuit design, specifically focusing on modeling techniques such as Bayesian inference and neural network (NN)-based surrogate models, optimization and sizing strategies, specification-driven predictive design, and artificial intelligence (AI)-assisted design automation for layout generation. Through an extensive survey of the existing literature, we analyze the effectiveness, strengths, and limitations of various ML approaches, identifying key trends and gaps within the current research landscape. Finally, the paper outlines potential future research directions aimed at advancing ML integration in AMS-ICs design, emphasizing the need for improved explainability, data availability, methodological rigor, and end-to-end automation.

A High-Efficiency and Versatile Reconfigurable Intelligent Surface Design Paradigm with Novel Topological Representation

This paper addresses fundamental aspects of analog design automation. Rather than following the present trends of basing the development of design automation tools on the existing expert knowledge of circuit topologies, proven circuit implementations and application examples, it emphasizes the need for the development of strategies and routines for generating specific solutions for design problems, starting from a definition of the required behaviour of a basic signal-processing function. The proposed design approach differs from the traditional approach in that it explicitly takes the signal information as a starting point for setting up performance requirements. As a prerequisite for true design automation, it thereby attempts to explicitly formulate all steps required to find adequate solutions, formulated in terms of information processing fidelity. It isolates the various performance aspects, tries to find optimization strategies for each of them individually, and subsequently, tries to find strategies to minimize their interaction. It concentrates first on three essential limitations of all physical systems: speed, noise and power, the three elements of Shannon’s formula for channel capacity. By attaching behavioral models to active devices with respect to each of these aspects in combination with the above mentioned strategies, one can quickly decide on the fundamental feasibility of a function, given a certain implementation technology. The strategies refer to circuit topology generation as well as to modification of the behaviour by applying error-reduction techniques in several subsequent hierarchical steps. During this process, other performance requirements such as those with respect to offsets, accuracy, non-linearity, etc., are taken into account. The routines refer to calculation methods needed for quick analysis and to the tools for the final verification.

The demand for computing resources to support design automation systems is growing dramatically due to VLSI design considerations. This demand has created a need for multiple computing systems to handle the escalated execution requirements of today's Design Automation tools. A distribution technique for executing such tools on multiple computing systems is discussed. The technique employs the definition of three execution modes: interactive foreground, dissociative foreground and batch. Cost and performance considerations are analyzed as they relate to the overall design automation system as well as individual job executions. An implementation of this technique is presented, including the treatment of common system interface, data management, and job submission techniques.

Within the context of the changing design requirements of digital systems spanning the semiconductor era, this paper describes the significant steps in the development of Design Automation technology in IBM. We cover the design tools which support the design of the electronic portion of such systems. The paper emphasizes the systems approaches taken and the topics of design verification, test generation, and physical design. Descriptions of the technical contributions and interactions which have led to the unique characteristics of IBM's Design Automation systems are included.

The high dynamism of all social processes and phenomena determines the formation of a new worldview system for humanity, the modification of the hierarchy of needs and values and challenges to the pace and quality of development. Solving highly complex tasks related to meeting modern requirements demands the use of the latest scientific solutions and tools. Therefore, robotics, automation, and digitalization, which are based on advanced intellectual technologies, are considered to be the driving forces behind the innovative and technological structure. Humanity also has to solve a number of complex problems related to ecology, the search for new sources of energy, materials, and technologies that are acceptable to society. High information technologies play a decisive role in solving these problems. Among information technologies, design automation occupies a special place in the educational process. Firstly, design automation is a discipline that incorporates many other modern information technologies. For example, the technical support for computer-aided design (CAD) systems is based on the use of computer networks and telecommunications technologies, and CAD uses personal computers and workstations. The mathematical support of CAD systems is distinguished by the richness and diversity of methods of computational mathematics, statistics, mathematical programming, discrete mathematics, and artificial intelligence. CAD software packages are among the most complex modern software systems based on operating systems, complex programming languages, and other modern CASE technologies. Secondly, knowledge of the basics of design automation and the ability to work with CAD tools are necessary for virtually any future design engineer. This article examines the issues and problems students encounter when studying high-tech disciplines. In today's environment, higher education institutions (HEIs) need a high-quality and technologically advanced new approach to training students at different levels of education. The introduction of high-tech disciplines into teaching allows for the rapid development and offering of a variety of individual assignments on a wide range of topics to students, taking into account their initial level of computer literacy. With the right approach, CAD can be an excellent basis for introducing project-based learning into the educational process. In addition, the use of automated design systems in the educational process, along with solving the main task, provides a number of additional educational effects. The combined effect of these factors significantly improves learning outcomes. This article discusses some of the distinctive features of CAD that have a positive impact on the effectiveness of training modern specialists.

With thousands of design books available, covering all the electrical and electronics fields of interest, Spectrum decided to identify some of the top books. The list on these pages is limited to five design areas, chosen because they are related to other articles in this issue. The books were recommended after consultation with experts in computer-aided design and modeling, design automation, general systems design techniques, microwave design techniques, and power systems design techniques and planning. Books in these areas cover (and often overlap) such disciplines as decision review, decision analysis, and pattern matching.

Image segmentation is an essential step in every practical image processing system. Current image segmentation algorithms suffer from a well known problem- They have poor performances on images different from the ones that were used in their initial development and training stages. In this paper we discuss a system concept for automatic design of segmentation algorithms based on image and objects metrics, and knowledge of image processing primitives.The proposed system concept makes makes use of Planning techniques, discussed in Artificial Intelligence.This paper provides the essential elements of the next generation Automatic Segmentation Design (ASD) systems that will not be scenario dependent. Applications of this concept include robotic vision, automatic target recognition, and image understanding systems.

The difficulties associated with designing, building, and controlling robots have led their development to a stasis: Applications are limited mostly to repetitive tasks with predefined behavior. Over the last few years we have been trying to address this challenge through an alternative approach: Rather than trying to control an existing machine or create a general-purpose robot, we propose that both the morphology and the controller should evolve at the same time. This process can lead to the automatic design of special-purpose mechanisms and controllers for specific short-term objectives. Here we provide a brief review of three generations of our recent research, which underlies the robots shown on the cover of this issue: Automatically designed static structures, automatically designed and manufactured dynamic electromechanical systems, and modular robots automatically designed through a generative DNA-like encoding.