Challenges and opportunities in computer-aided molecular design

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Recent Updates Regarding the Antiproliferative Activity of Galium verum Extracts on A375 Human Malignant Melanoma Cell Line

The biological activity of Galium verum herba was exerted on various tumor cell lines with incredible results, but their potential effect on malignant melanoma has not been established yet. Therefore, the current study was structured in two directions: (i) the investigation of the phytochemical profile of diethyl ether (GvDEE) and butanol (GvBuOH) extracts of G. verum L. and (ii) the evaluation of their biological profile on A375 human malignant melanoma cell line. The GvDEE extract showed an FT-IR profile different from the butanol one, with high antioxidant capacity (EC50 of GvDEE = 0.12 ± 0.03 mg/mL > EC50 of GvBuOH = 0.18 ± 0.05 mg/mL). The GvDEE extract also showed antimicrobial potential, especially against Gram-positive bacteria strains, compared to the butanol extract, which has no antimicrobial activity against any bacterial strain tested. The results regarding the antitumor potential showed that both extracts decreased A375 cell viability largely (69% at a dose of 55 µg/mL of the GvDEE extract). Moreover, both extracts induce nuclear fragmentation by forming apoptotic bodies and slight chromatin condensation, which is more intense for GvDEE. Considering the results, one can state that the Galium verum herba possesses antitumor effects on the A375 human malignant melanoma cell line, a promising phytocompound for the antitumor approach to skin cancer.

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks

Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynthesis model based on Modern Hopfield Networks, which learn an encoding of both molecules and reaction templates in order to predict the relevance of templates for a given molecule. The template representation allows generalization across different reactions and significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed up to orders of magnitude faster than baseline methods, we improve or match the state-of-the-art performance for top-k exact match accuracy for k ≥ 3 in the retrosynthesis benchmark USPTO-50k. Code to reproduce the results is available at github.com/ml-jku/mhn-react.

Computer-Aided Framework for the Design of Optimal Bio-Oil/Solvent Blend with Economic Considerations

A major obstacle in utilising pyrolysis bio-oil as biofuel is its relatively low heating value, high viscosity, and non-homogeneity. Solvent addition is a simple yet practical approach in upgrading pyrolysis bio-oil. However, most solvents are often manufactured as specialty chemicals, and thus, this leads to a high production cost of solvents. It is crucial for the designed solvent-oil blend to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. Hence, the objective of this work is to generate feasible solvent candidates by solving this multi-objective optimisation (MOO) problem via a computer-aided molecular design (CAMD) approach. Initially, an optimisation model was developed to identify potential solvents that satisfied the predefined targeted properties. Next, a MOO model was developed via a fuzzy optimisation approach to identify the trade-off between profitability and heating value of the solvent-oil blend. A pricing model was employed to estimate the profitability of the solvent-oil blend. The production of bio-oil in a pyrolysis plant was used to illustrate the applicability of the pricing model. Lastly, phase stability analysis was conducted to ensure the stability and miscibility of the solvent-oil blend. With the developed framework, a promising and cost-effective solvent-oil blend can be generated while displaying optimal biofuel properties.

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

BACKGROUND

Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space.

METHODS

Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process.

RESULTS

We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis.

CONCLUSIONS

Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Integrated Design of Working Fluid Mixtures and Absorption Refrigeration Cycles

This work presents a CAMD (computer-aided molecular design) approach for the design of working fluid mixtures used in ABR (absorption refrigeration) cycles. Compared to previous works, the proposed approach introduces two major improvements. It employs for the first time an ABR process model in the course of CAMD, hence enabling the evaluation of the generated mixtures considering process performance indicators. It enables for the first time the simultaneous generation and evaluation of molecular structures for both refrigerants and absorbents. The employed model and CAMD optimization problem formulation incorporates major ABR operational driving forces pertaining to efficient refrigeration, sufficient solubility of mixture components and ease of separation in the generator. The approach employs a multicriteria assessment methodology both during CAMD and for the evaluation of selected mixtures using a more rigorous ABR model at a second stage. The work identifies novel mixtures, with Acetaldehyde/2-Methoxyethyl acetate and Acetaldehyde/Methanediol exhibiting the highest performance. The latter exhibits 3% higher COP (coefficient of performance) and cooling output than the reference mixture NH3/H2O, whereas it operates at 87 and 89% lower high and low cycle pressures. The novel mixtures are also compared with novel mixtures previously identified through a heuristic approach by the authors. The latter mixtures indicate overall higher ABR performance but similar or worse performance in safety, health and environmental impact indices. Further performance improvements may be achieved by including into CAMD additional chemical groups to be able to simulate the complex absorbent structures available in published works.

Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing

Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality. Considering their significant effects, systematic methods for the optimal selection and design of materials are essential. The conventional synthesis-and-test method for materials development is inefficient and costly. Additionally, the performance of the resulting materials is usually limited by the designer’s expertise. During the past few decades, computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes. This article selectively focuses on two important process functional materials, namely heterogeneous catalyst and gas separation agent. Theoretical methods and representative works for computational screening and design of these materials are reviewed.

Design and performance optimisation of detergent product containing binary mixture of anionic-nonionic surfactants

The manufacture of detergent products such as laundry detergents, household cleaners and fabric softeners are of increasing interest to the consumer oriented chemical industry. Surfactants are the most important ingredient in detergent formulations, as they are responsible for the bulk of the cleaning power. In this research, a methodology has been developed to design a detergent product using computational tools. Different surfactant systems, such as single anionic, single nonionic, and binary mixtures of anionic-nonionic surfactants are covered in this work. Important surfactant properties such as critical micelle concentration (CMC), cloud point (CP), hydrophilic-lipophilic balance (HLB) and molecular weight (MW) have been identified. A group contribution (GC) method with the aid of computer modelling was used to determine the CMC, CP, and MW of surfactant molecules. The design of a surfactant molecule can be formulated as a multi-objective optimization problem that tradeoffs between CMC, CP, HLB and MW. Consequently, a list of plausible nonionic surfactant structures has been developed with the selected surfactant being incorporated into a binary surfactant mixture. Additives such as antimicrobial agents, anti-redeposition agents, builders, enzymes, and fillers were also considered and incorporated into a hypothetical detergent formulation together with the binary surfactant mixture. The typical ingredients and their compositions in detergent formulations are presented in the final stage of the detergent product design.

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

Background

Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.

Results

By generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.

Conclusions

We developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2514-6) contains supplementary material, which is available to authorized users.

1-Butanol as a Solvent for Efficient Extraction of Polar Compounds from Aqueous Medium: Theoretical and Practical Aspects

The extraction of polar molecules from aqueous solution is a challenging task in organic synthesis. 1-Butanol has been used sporadically as an eluent for polar molecules, but it is unclear which molecular features drive its efficiency. Here, we employ free energy simulations to study the partitioning of 15 solutes between water and 1-butanol. The simulations demonstrate that the high affinity of polar molecules to the wet 1-butanol phase is associated with its nanostructure. Small inverse micelles of water are able to accommodate polar solutes and locally mimic an aqueous environment. We verify the simulations based on partition coefficients between water and 1-octanol, and include a blind prediction of the water/1-butanol partition coefficient of cyclohexane-1,2-diol. The calculations are in excellent agreement with experiment, reaching root-mean-square deviations below 0.7 kcal/mol. Actual extractions of cyclohexane-1,2-diol from buffer solutions that mimic cell lysates and suspensions in biocatalytic reactions further exemplify our findings. The yields highlight that extractions with 1-butanol can be significantly more efficient than the conventional protocol based on ethyl acetate.

Advances in chemical product design

The nature of chemical product design problems is diverse and multidisciplinary. It involves many design issues such as project management, market study, product design, process design, and economic analysis for better organizing the product design project and achieving better products. This article provides an overview of chemical product design with a multidisciplinary hierarchical framework including all the design issues and tasks. Each of the design issues and tasks are introduced and discussed, methods and tools are summarized and compared, challenges and perspectives are presented to help the chemical product design researchers on finding more novel, innovative and sustainable products, by the combined effort from academia and industry to develop a systematic generic framework, and tools including product simulator, process simulator, database manager, modeling tool, and templates for design problems.

Tuning the HOMO-LUMO Energy Gap of Small Diamondoids Using Inverse Molecular Design

Functionalized diamondoids show great potential as building blocks for various new optoelectronic applications. However, until now, only simple mono and double substitutions were investigated. In this work, we considered up to 10 and 6 sites for functionalization of the two smallest diamondoids, adamantane and diamantane, respectively, in search for diamondoid derivatives with a minimal and maximal HOMO-LUMO energy gap. To this end, the energy gap was optimized systematically using an inverse molecular design methodology based on the best-first search algorithm combined with a Monte Carlo component to escape local optima. Adamantane derivatives were found with HOMO-LUMO gaps ranging from 2.42 to 10.63 eV, with 9.45 eV being the energy gap of pure adamantane. For diamantane, similar values were obtained. The structures with the lowest HOMO-LUMO gaps showed apparent push-pull character. The push character is mainly formed by sulfur or nitrogen dopants and thiol groups, whereas the pull character is predominantly determined by the presence of electron-withdrawing nitro or carbonyl groups assisted by amino and hydroxyl groups via the formation of intramolecular hydrogen bonds. In contrast, maximal HOMO-LUMO gaps were obtained by introducing numerous electronegative groups.

New Vistas in Chemical Product and Process Design

Design of chemicals-based products is broadly classified into those that are process centered and those that are product centered. In this article, the designs of both classes of products are reviewed from a process systems point of view; developments related to the design of the chemical product, its corresponding process, and its integration are highlighted. Although significant advances have been made in the development of systematic model-based techniques for process design (also for optimization, operation, and control), much work is needed to reach the same level for product design. Timeline diagrams illustrating key contributions in product design, process design, and integrated product-process design are presented. The search for novel, innovative, and sustainable solutions must be matched by consideration of issues related to the multidisciplinary nature of problems, the lack of data needed for model development, solution strategies that incorporate multiscale options, and reliability versus predictive power. The need for an integrated model-experiment-based design approach is discussed together with benefits of employing a systematic computer-aided framework with built-in design templates.