Structure-property Relationships Research Articles

Evaluation of Point Group Symmetry in Lanthanide(III) Complexes: A New Implementation of a Continuous Symmetry Operation Measure with Autonomous Assignment of the Principal Axis.

The structure of molecular systems dictates the physical properties, and symmetry is the determining factor for all electronic properties. This makes group theory a powerful tool in quantum mechanics to compute molecular properties. For inorganic compounds, the coordination geometry has been estimated as idealized polyhedra with high symmetry, which, through ligand field theory, provides predictive capabilities. However, real samples rarely have ideal symmetry, and although continuous shape measures (CShM) can be used to evaluate deviation from an ideal reference structure σideal, this often fails for lanthanide(III) complexes with high coordination numbers, no obvious choice of principal axes, and no obvious reference structure. In lanthanide complexes, the unique electronic structures and associated properties are intricately tied to the symmetry around the lanthanide center. Therefore, robust methodologies to evaluate and estimate point group symmetry are instrumental for building structure-property relationships. Here, we have demonstrated an algorithmic approach that orients a molecular structure Q in the best possible way to the symmetry axis of any given point group G and computes a deviation from the ideal symmetry σsym(G,Q). This approach does not compute the deviation from an ideal reference system, but the intrinsic deviation in the structure induced by symmetry operations. If the structure contains the symmetry operation, there is no deviation and σsym(G,Q) = 0. The σsym deviation is generated from all of the symmetry operation ÔS in a point group G using the most correct orientation of the sample structure in each group G. The best orientation is found by an algorithm that minimizes the orientation of the structure with respect to G. To demonstrate the methodology, we have investigated the structure and symmetry of 8- and 9-coordinated lanthanide(III) aqua complexes and correlated the luminescence from 3 europium(III) crystals to their actual symmetry. To document the methodology, the approach has been tested on 26 molecules with different symmetries. It was concluded that the method is robust and fully autonomous.

Polycatenanes Formed of Self‐Assembled Metal‐Organic Cages

AbstractPoly‐[n]‐catenanes (PCs) self‐assembled of three‐dimensional (3D) metal organic cages (MOCs) (hereafter referred to as PCs‐MOCs) are a relatively new class of mechanically interlocked molecules (MIMs) that combine the properties of MOCs and polymers. The synthesis of PCs‐MOCs is challenging because of the difficulties associated with interlocking MOCs, the occurrence of multiple weak supramolecular electrostatic interactions between cages, and the importance of solvent templating effects. The high density of mechanical bonds interlocking the MOCs endows the MOCs with mechanical and physical properties such as enhanced stability, responsive dynamic behavior and low solubility, which can unlock new functional properties. In this Minireview, we highlight the benefit of interlocking MOCs in the formation of PCs‐MOCs structures as well as the synthetic approaches exploited in their preparation, from thermodynamic to kinetic methods, both in the solution and solid‐states. Examples of PCs‐MOCs self‐assembled from various types of nanosized cages (i.e., tetrahedral, trigonal prismatic, octahedral and icosahedral) are described in this article, providing an overview of the research carried out in this area. The focus is on the structure–property relationship with examples of functional applications such as electron conductivity, X‐ray attenuation, gas adsorption and molecular sensing. We believe that the structural and functional aspects of the reviewed PCs‐MOCs will attract chemists in this research field with great potential as new functional materials in nanotechnological disciplines such as gas adsorption, sensing and photophysical properties such as X‐ray attenuation or electron conductivity.

Unusual negative thermal expansion of inter-cluster –C–BC–C– bond in carbon rich boron carbide observed using in situ x-ray diffraction technique

Temperature dependent bonding behavior plays a significant role in deciding properties of high temperature ceramics like boron carbide. However, few studies to date have addressed the physical properties of this class of materials with respect to their temperature dependent bonding nature. In addition, materials with the flexibility to accommodate variations in interatomic bonding and lattice vibrations over a wide range of temperatures are less known. In this work, temperature dependent structural analyses of carbon-rich boron carbide microflakes using in situ powder x-ray diffraction techniques (up to 1000 °C) supported by transmission electron microscopy measurements reveal that while most bonds in the rhombohedral structure increase in length with temperature; there is no change in certain bond lengths. However, there is an unusual decrease in length (∼1.03%) of the inter-cluster –C–(central boron)BC–C– without any polyhedral redistribution. This is accompanied by an increase in lattice vibrations without significant alteration to the crystal structure over the wide temperature range studied. Temperature dependent micro-Raman experiments further confirmed the above observations. The above bonding behavior could be directly correlated to the trends in reported results of high temperature conductivity via the model of hole hopping through specific atomic positions of the rhombohedral framework, thus opening the scope to investigate structure–property relationships in high temperature functional materials.

Thermally Stable and Transparent Polyimide Derived from Side-Group-Regulated Spirobifluorene Unit for Substrate Application.

Advancements in flexible electronic technology, especially the progress in foldable displays and under-display cameras (UDC), have created an urgent demand for high-performance colorless polyimide (CPI). However, current CPIs lack sufficient heat resistance for substrate applications. In this work, four kinds of rigid spirobifluorene diamines are designed, and the corresponding polyimides are prepared by their condensation with 5,5'-(perfluoropropane-2,2-diyl) bis(isobenzofuran-1,3-dione) (6FDA) or 9,9-bis(3,4-dicarboxyphenyl) fluorene dianhydride (BPAF). The rigid and conjugated spirobifluorene units endow the polyimides with higher glass transition temperature(Tg) ranging from 356 to 468 °C. Their optical properties are regulated by small side groups and spirobifluorene structure with a periodically twisted molecular conformation. Consequently, a series of CPIs with an average transmittance ranging from 75% to 88% and a yellowness index (YI) as low as 2.48 are obtained. Among these, 27SPFTFA-BPAF presents excellent comprehensive performance, with a Tg of 422 °C, a 5wt.% loss temperature (Td5) of 562 °C, a YI of 3.53, and a tensile strength (δmax) of 140MPa, respectively. The mechanism underlying the structure-property relationship is investigated by experimental comparison and theoretical calculation, and the proposed method provides a pathway for designing highly rigid conjugated CPIs with excellent thermal stability and transparency for photoelectric engineering.

Investigating the potential of pyrazine dioxide based-compounds as organic electrodes for batteries.

Understanding structure-property relationship in redox-active molecular species is of central importance in various fields, including many medicinal and chemical applications. The quest for performant organic electrodes in the context of energy storage calls for pioneering studies to develop new and possibly optimal materials. Beyond modifying the molecular design of the existing compounds through functionalization, expansion of the search enabling the advent of efficient new backbones can potentially lead to breakthroughs in this research area. The number of already identified families able to constitute negative organic electrodes is much lower than that of their positive counterparts, which calls for finding ways to bridge this gap. To expand the dataset of known predicted redox potentials and in view of reaching an educated guess about the abilities of some eventual new redox active electrodes, we examined the properties of pyrazine N,N'-dioxide (PZDO) and its fully methylated functionalized derivative (TeMePzDO). The aspects and mechanisms driving the various features characteristic of these compounds were unraveled through molecular and periodic DFT calculations combined with accurate electronic structure analysis. The predicted molecular redox/crystalline intercalation potentials lead to the classification of PZDO and TeMePzDO systems within the class of negative electrodes, with features that are significantly appealing compared to those of some existing systems with backbones suited for such kind of application.

The Chemistry–Process–Structure Relationships of a Functionally Graded Ti-6Al-4V/Ti-1B Alloy Processed with Laser-Engineered Net Shaping Creates Borlite

We quantify the chemistry–process–structure–property relationships of a Ti-6Al-4V alloy in which titanium-boron alloy (Ti-B) was added in a functionally graded assembly through a laser-engineered net shaping (LENS) process. The material gradient was made by pre-alloyed powder additions to form an in situ melt of the prescribed alloy concentration. The complex heterogeneous structures arising from the LENS thermal history are completely discussed for the first time, and we introduce a new term called “Borlite”, a eutectic structure containing orthorhombic titanium monoboride (TiB) and titanium. The β-titanium grain size decreased nonlinearly until reaching the minimum when the boron weight fraction reached 0.25%. Similarly, the transformed α-titanium grain size decreased nonlinearly until reaching the minimum level, but the grain size was approximately 2 μm when the boron weight fraction reached 0.6%. Alternatively, the α-titanium grain size increased nonlinearly from 1 to 5 μm as a function of the aluminum concentration increasing from 0% to 6% aluminum by weight and vanadium increasing from 0% to 4% by weight. Finally, the cause–effect relationships related to the creation of unwanted porosity were quantified, which helps in further developing additively manufactured metal alloys.

Compressive Properties of Additively Manufactured Metal-Reinforced PLA and ABS Composites

The development of multi-material filaments has enabled fused filament fabrication-based additive manufacturing to address demand for high-performance lightweight multifunctional components. In this study, polylactic acid (PLA) and acrylonitrile butadiene styrene processing–structure–property)-based filaments with metallic reinforcements of magnetic iron (MI), stainless steel (SS), bronze (Br), copper (Cu), Bismuth (Bi), and Tungsten (W) were investigated to elucidate their complex processing–structure–property relationships. The microstructure of 3D-printed materials were characterized by microscopy and analyzed to determine the metal cross-sectional area percentage and the relationship between metal reinforcement, the polymer matrix, and porosity. Compression testing was conducted in directions parallel and perpendicular to the build direction in order to evaluate the effect of orientation and metal reinforcement on the mechanical properties. Three-dimensional-printed specimens experienced either fracture through print layers or layer-wise interfacial rupture for loads applied perpendicular and parallel to the print layers, respectively. A dependence of yield strength on loading orientation was observed for Br-PLA, Cu-PLA, SS-PLA, Bi-ABS, and W-ABS; however, MI-PLA and pure ABS specimens did not exhibit this sensitivity. Metal reinforcement also influenced the magnitude of compressive yield strength, with MI-PLA and SS-PLA demonstrating increased strength over Br-PLA and Cu-PLA, while ABS demonstrated increased strength over Bi-ABS and W-ABS. These results demonstrate the importance of considering orientation in printing and applications, the trade-off between various metallic reinforcements for added multifunctionality, and the potential of these tailored polymer composites for novel 3D-printed structures.

An Atom-Precise Understanding of DNA-Stabilized Silver Nanoclusters.

ConspectusDNA-stabilized silver nanoclusters (AgN-DNAs) are sequence-encoded fluorophores. Like other noble metal nanoclusters, the optical properties of AgN-DNAs are dictated by their atomically precise sizes and shapes. What makes AgN-DNAs unique is that nanocluster size and shape are controlled by nucleobase sequence of the templating DNA oligomer. By choice of DNA sequence, it is possible to synthesize a wide range of AgN-DNAs with diverse emission colors and other intriguing photophysical properties. AgN-DNAs hold significant potential as "programmable" emitters for biological imaging due to their combination of small molecular-like sizes, bright and sequence-tuned fluorescence, low toxicities, and cost-effective synthesis. In particular, the potential to extend AgN-DNAs into the second near-infrared region (NIR-II) is promising for deep tissue imaging, which is a major area of interest for advancing biomedical imaging. Achieving this goal requires a deep understanding of the structure-property relationships that govern AgN-DNAs in order to design AgN-DNA emitters with sizes and geometries that support NIR-II emission.In recent years, major advances have been made in understanding the structure and composition of AgN-DNAs, enabling new insights into the correlation of nanocluster structure and photophysical properties. These advances have hinged on combined innovations in mass characterization and crystallography of compositionally pure AgN-DNAs, together with combinatorial experiments and machine learning-guided design. A combined approach is essential due to the major challenge of growing suitable AgN-DNA crystals for diffraction and to the labor-intensive nature of preparing and solving the molecular formulas of atomically precise AgN-DNAs by mass spectrometry. These approaches alone are not feasibly scaled to explore the large sequence space of DNA oligomer templates for AgN-DNAs.This account describes recent fundamental advances in AgN-DNA science that have been enabled by high throughput synthesis and fluorimetry together with detailed analytical studies of purified AgN-DNAs. First, short introductions to nanocluster chemistry and AgN-DNA basics are presented. Then, we review recent large-scale studies that have screened thousands of DNA templates for AgN-DNAs, leading to discovery of distinct classes of these emitters with unique cluster core compositions and ligand chemistries. In particular, the discovery of a new class of chloride-stabilized AgN-DNAs enabled the first ab initio calculations of AgN-DNA electronic structure and present new approaches to stabilize these emitters in biologically relevant conditions. Near-infrared (NIR) emissive AgN-DNAs are also found to exhibit diverse structures and properties. Finally, we conclude by highlighting recent proof-of-principle demonstrations of NIR AgN-DNAs for targeted fluorescence imaging. Continued efforts may future push AgN-DNAs into the tissue transparency window for fluorescence imaging in the NIR-II tissue transparency window.

Virtual node graph neural network for full phonon prediction.

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility.

Quantitative Characterization of RCA-based DNA Hydrogels - Towards Rational Design.

DNA hydrogels hold significant promise for biomedical applications and can be synthesized through enzymatic Rolling Circle Amplification (RCA). Due to the exploratory nature of this emerging field, standardized RCA protocols specifying the impact of reaction parameters are currently lacking. This study varied template sequences and reagent concentrations, evaluating RCA synthesis efficiency and hydrogel mechanical properties through quantitative PCR (qPCR) and indentation measurements, respectively. Primer concentration and stabilizing additives showed minimal impact on RCA efficiency, while changes in polymerase and nucleotide concentrations had a stronger effect. Concentration of the circular template exerted the greatest influence on RCA productivity. An exponential correlation between hydrogel viscosity and DNA amplicon concentration was observed, with nucleobase sequence significantly affecting both amplification efficiency and material properties, particularly through secondary structures. This study suggests that combining high-throughput experimental methods with structural folding prediction offers a viable approach for systematically establishing structure-property relationships, aiding the rational design of DNA hydrogel material systems.

Quamtum Mechanical Study on Structural and Electronic Properties of Tert-Butyl Based Bridged Dithiophene Oxide Derivatives

Computational modeling is vital to designing and creating organic semiconductors used in solar cells, organic field-effect transistors, and other areas. This work studied the structural and electronic features of a group of substituted tert-butyl bridged dithiophene oxide derivatives using Density Functional Theory (DFT) calculations. Geometry improvements were carried out using the B3LYP hybrid functional and the 6-31G(d,p) basis set in Gaussian 09. Molecular shapes, bond lengths, bond angles, and dihedral angles were studied to find out how substitution patterns change the packing and conformation of molecules. Energy levels, distribution, and makeup of frontier molecular orbitals were found. This calculation also included finding other electronic qualities, such as electronic charges, dipole moments, and polarizabilities. Findings show that tert-butyl substitution makes the molecular backbone stiffer and limits its ability to twist compared to similar molecules that have not been replaced. The chemical geometry stays mostly the same when electron-withdrawing or electron-donating substituents are added to the tert- butyl groups. Nevertheless, the strength and location of substituents have a significant impact on frontier orbital energies. The HOMO-LUMO gap grew significantly when the nitro or cyano groups firmly pulled electrons away from derivatives. For successful charge transport, electron density plots show that the HOMO and LUMO are mainly located on the thiophene and substituent moieties, respectively. Molecular dipole moments are also strongly affected by the electronic features of substituents. This research shows how to change the optoelectronic properties of tert-butyl-based dithiophene oxide derivatives and how their structure-property relationships can be improved. The results help makes new organic semiconductors that work better in various electronic and optoelectronic uses.

Visible-Light-Activated Heteroaryl Azoswitches: Toward a More Colorful Future.

Azobenzenes (Ph-N═N-Ph) are known as the most widely studied molecular photoswitches, and the recent rise of azoheteroarenes (Het-N═N-Ph or Het-N═N-Het) offers great opportunities to advance this already mature field. A common limitation is that azo-switches generally require harmful UV light for activation, which hinders their application across various fields. Despite great efforts in developing visible-light azobenzenes over the past few decades, the potential of visible-light heteroaryl azoswitches remains largely unexplored. This Perspective summarizes the state-of-the-art advancements in visible-light heteroaryl azoswitches, covering molecular design strategies, the structure-property relationship, and potential applications. We highlight the distinctive advantages of azoheteroarenes over azobenzenes in the research and development of visible-light switches. Furthermore, we discuss the opportunities and challenges in this emerging field and propose potential solutions to address crucial issues such as spectral red-shift and thermal half-life. Through this Perspective paper, we aim to provide inspiration for further exploration in this field, in anticipation of the growing prosperity and bright future of visible-light azoheteroarene photoswitches.

Phase stability, piezoelectricity, and ferroelectricity in nitride short-period superlattices

Improving piezoelectric and ferroelectric responses of group III-nitrides is desired for their potential applications in the emerging microelectromechanical-based systems. One possible approach to realize the optimization and control of functionalities is to bring together compounds with different properties to form the ordered multilayer superlattices. In this work, we systematically investigate the phase stability, piezoelectricity, and ferroelectricity in a class of wurtzite-structure-derived nitride superlattices with a periodic alternation of chemically and/or structurally different layers. The structural heterogeneity and phase stability of the ordered wurtzite-structure-derived superlattices are intimately related to the ionic radii mismatch between the substitutional compounds and the parent nitrides. Moreover, the internal structural distortion of the nitride superlattices has a crucial impact on the ferroelectricity and piezoelectricity, namely, piezoelectric and ferroelectric responses become increasingly enhanced as the buckled atomic layers becomes flatter. This work offers fundamental physical insights into the structure–property relationships in nitride superlattices and may propose some material design strategies for achieving high-performance materials with desired responses.

Poly(ester imide)s with Low Linear Coefficients of Thermal Expansion and Low Water Uptake (VIII): Structure–Flame Retardancy Relationship

A series of ester-linked tetracarboxylic dianhydrides containing multiple para-phenylene units (TA-pPhs) was synthesized to obtain novel modified polyimides, namely poly(ester imide)s (PEsIs). The flame retardancy and film toughness of PEsIs tended to deteriorate with the structural extension of the repeating units (or monomers) via ester groups. To identify the structural factors necessary for achieving the highest flame retardancy rank (UL-94, V-0), we systematically investigated the structure–property relationships of a series of TA-pPh-based PEsIs. Among them, a PEsI derived from para-quaterphenylene-containing TA-pPh (TA-DPQP) and p-phenylenediamine (p-PDA) exhibited the best property combination, featuring an extremely high glass transition temperature (Tg), very low linear coefficient of thermal expansion (CTE), low water uptake (WA), ultralow linear coefficient of humidity (hygroscopic) expansion (CHE), unexpectedly high film toughness, and excellent flame retardancy (V-0 rank). Moreover, we examined the effects of substituents of TA-pPh and discussed the mode of action for the increased film toughness. This study also investigated the structure–property relationship for a series of PEsIs derived from isomeric naphthalene-containing tetracarboxylic dianhydrides. Some of the PEsIs obtained in this study, such as the TA-DPQP/p-PDA system, hold promise as novel high-temperature dielectric substrates for use in flexible printed circuits.

A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R2) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), and 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] quinoxaline (Q6), show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

Structure–Property Relationships in Auxetic Liquid Crystal Elastomers—The Effect of Spacer Length

Auxetics are materials displaying a negative Poisson’s ratio, i.e., getting thicker in one or both transverse axes when subject to strain. In 2018, liquid crystal elastomers (LCEs) displaying auxetic behaviour, achieved via a biaxial reorientation, were first reported. Studies have since focused on determining the physics underpinning the auxetic response, with investigations into structure–property relationships within these systems so far overlooked. Herein, we report the first structure–property relationships in auxetic LCEs, examining the effect of changes to the length of the spacer chain. We demonstrate that for LCEs with between six and four carbons in the spacer, an auxetic response is observed, with the threshold strain required to achieve this response varying from 56% (six carbon spacers) to 81% (four carbon spacers). We also demonstrate that Poisson’s ratios as low as −1.3 can be achieved. Further, we report that the LCEs display smectic phases with spacers of seven or more carbons; the resulting internal constraints cause low strains at failure, preventing an auxetic response. We also investigate the dependence of the auxetic threshold on the dynamics of the samples, finding that when accounting for the glass transition temperature of the LCEs, the auxetic thresholds converge around 56%, regardless of spacer length.

Stabilization of Short- and Long-Range Magnetic Orderingthrough the Cooperative Effect of 1D [CuO4] Chainsand [CuO2X2] Quadrilateral in Quasi-1D Spin-1/2 Systems.

Quasi-1D chain antiferromagnetswith reduced structural dimensionality area rich playground for investigating novel quantum phenomena. We report the synthesis, crystal structure, and magnetism of two novel quasi-1D antiferromagnets, β-PbCu2(TeO3)2Cl2 (I) and PbCu2(TeO3)2Br2 (II).Their magnetic frameworks are constructed via Cu-based quasi-1D [Cu(2)O4]zigzagchains with square-planar [Cu(1)O2X2] (X = Cl or Br) separated among 1D chains. Specific heat measurements show l peaks at ~9 K and ~19 Kfor I and II, respectively. Moreover, both broad maximums (χmax = 90 K for I and 80 K for II) and small kinks (TN ≈ 9 K for I and 19 K for II) have been observed in magnetic susceptibility measurements of I and II. Bonner-Fisher model fitting, and theoretical analyses were performed to evaluate the magnetic exchange interactions. Our experimental and theoretical results and structure-properties relationship analysis reveal the coexistence of short-and long-range magnetic ordering from the cooperative effect of 1D [CuO4]chains and [CuO2X2] quadrilateral.

Stabilization of Short- and Long-Range Magnetic Orderingthrough the Cooperative Effect of 1D [CuO4] Chainsand [CuO2X2] Quadrilateral in Quasi-1D Spin-1/2 Systems.

Quasi-1D chain antiferromagnetswith reduced structural dimensionality area rich playground for investigating novel quantum phenomena. We report the synthesis, crystal structure, and magnetism of two novel quasi-1D antiferromagnets, β-PbCu2(TeO3)2Cl2 (I) and PbCu2(TeO3)2Br2 (II).Their magnetic frameworks are constructed via Cu-based quasi-1D [Cu(2)O4]zigzagchains with square-planar [Cu(1)O2X2] (X = Cl or Br) separated among 1D chains. Specific heat measurements show l peaks at ~9 K and ~19 Kfor I and II, respectively. Moreover, both broad maximums (χmax = 90 K for I and 80 K for II) and small kinks (TN ≈ 9 K for I and 19 K for II) have been observed in magnetic susceptibility measurements of I and II. Bonner-Fisher model fitting, and theoretical analyses were performed to evaluate the magnetic exchange interactions. Our experimental and theoretical results and structure-properties relationship analysis reveal the coexistence of short-and long-range magnetic ordering from the cooperative effect of 1D [CuO4]chains and [CuO2X2] quadrilateral.

Application of machine learning models for property prediction to targeted protein degraders

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.

Application of machine learning models for property prediction to targeted protein degraders

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and make property predictions for new molecules. With the advent of modalities such as targeted protein degraders (TPD), the applicability of QSPR models is questioned and ML usage in TPD-centric projects remains limited. Herein, ML models are developed and evaluated for TPDs’ property predictions, including passive permeability, metabolic clearance, cytochrome P450 inhibition, plasma protein binding, and lipophilicity. Interestingly, performance on TPDs is comparable to that of other modalities. Predictions for glues and heterobifunctionals often yield lower and higher errors, respectively. For permeability, CYP3A4 inhibition, and human and rat microsomal clearance, misclassification errors into high and low risk categories are lower than 4% for glues and 15% for heterobifunctionals. For all modalities, misclassification errors range from 0.8% to 8.1%. Investigated transfer learning strategies improve predictions for heterobifunctionals. This is the first comprehensive evaluation of ML for the prediction of absorption, distribution, metabolism, and excretion (ADME) and physicochemical properties of TPD molecules, including heterobifunctional and molecular glue sub-modalities. Taken together, our investigations show that ML-based QSPR models are applicable to TPDs and support ML usage for TPDs’ design, to potentially accelerate drug discovery.