Opening ceremony Room: Lacture room 124

The utility of the four pillars of mathematical chemistry & QSAR in the service of the seven pillars of life: A novel perspective

• Subhash C. Basak (University of Minnesota Duluth, USA)

Room: Lacture room 124

‘Small and big' in computational modelling of toxicological endpoints

• Marjan Vračko (National Institute of Chemistry, Ljubljana, Slovenia)

Abstract: In the era of traditional toxicology, a large amount of data was collected, and summarised in various databases (also called "small data"). On this basis, many QSAR (Quantitative Structure-Activity Relationship) models have been developed to extend the knowledge of toxicity to a larger chemical space. On the other hand, we have a large body of "big data", i.e. high-throughput screening results and –omics results. It is a particular challenge to compress the "big and small" data and integrate it into a single model. The focus is on the presentation of 'big data' (omic data and high-throughput screening data) and the application of Counter-propagation Artificial Neural Networks (CPANN). CPANN represent one of the fundamental algorithms of Artificial Intelligence (AI). The focus is on its clustering and classification capabilities. As a first example, we present chemometric studies performed on proteomic data. The aim is to investigate the correlation between proteomic data and different in vitro endpoints and to gain insights into the mechanisms of action [1]. The second example is the analysis of genomic data, where we present the clustering of Zika, MERS, SARS and Covid-19 virus data with respect to the geographical origin of the viruses [2]. The third example is the modelling of binding affinity to thyroid receptors for a large data set (over 7000 compounds). The CPANN method is used for QSAR modelling and clustering of this large dataset. References: 1. M. Vračko, S.C. Basak & F. Witzmann, Chemometrical analysis of proteomics data obtained from three cell types treated with multiwalled carbon nanotubes and TiO2 nanobelts. SAR&QSAR Environ. Res. 2018, 29, 567-577. 2. M. Vračko, S. C. Basak, T Dey, A. Nandy, Cluster Analysis of Coronavirus Sequences using Computational Sequence Descriptors: With Applications to SARS, MERS and SARS-CoV-2 (CoVID-19). Curr. Comput.Aided Drug Des. 2021,17, 936-945.

Digital Chemistry as an Interplay of Chemistry, Mathematics and Informatics

• Denis Sabirov (Institute of Petrochemistry and Catalysis of Russian Academy of Sciences, Russia)

Room: Lacture room 124 Abstract: In this talk, we briefly discuss the birth of digital chemistry, which is a rising field and continuation of mathematical chemistry. We try distinguishing these two chemistries based on the comparison of the features of mathematical and digital models. The first ones commonly operate with low number of parameters and universal whereas, in contrast, their digital counterparts are multi-parameter and, hence, too accurate to be universal. The role of structural (mainly topological and information-theoretic) descriptors is discussed in the context of digitalizing chemical objects (molecules, molecular ensembles, and chemical reactions) and chemical processes (elementary and multi-stage chemical reactions). Representing complex chemical reactions as hypergraphs is also considered. As digital models are peculiar, they require special methods for treating, analysis and exploiting. Hence, arises the necessity of using fresh methodologies, operating with large data arrays, based on artificial neural networks, machine learning, and large linguistic models. The theses are exemplified with the works of our group and our colleagues: Profs S.C. Basak, S.H. Bertz, V.I. Sokolov, N.S. Zefirov, G. Restrepo, V.P. Ananikov, I.Ugi, S.V. Krivovichev, A.R. Oganov and other specialists working in the field.

Integrative Machine Learning and Structural Dynamics Approaches to Decipher Phenotypic Outcomes of Oncoprotein Missense Mutations in Cancer and Neurodevelopmental Disorders

• Guang Hu (School of Life Sciences, Suzhou Medical College of Soochow University, China)

Room: Lacture room 124 Abstract: Missense mutations in oncogenic proteins such as PTEN, PI3Kα, MEK1/2, SHP2, and RAS can lead to remarkably different phenotypes, including cancer and neurodevelopmental disorders (NDDs). A key challenge is to explain why mutations in the same protein result in divergent clinical outcomes. Here, we present integrative machine learning (ML) and structural dynamics approaches to address this problem. The first part introduces protPheMut, an interpretable ML tool that integrates sequence, biophysical, and network-dynamics features, enabling accurate classification of cancer- versus NDD-associated mutations across multiple proteins. Compared with existing predictors, protPheMut achieves superior accuracy and provides mechanistic insights through SHAP-based model explanations. The second part focuses on PTEN, a paradigmatic gene shared by cancer and autism spectrum disorder (ASD). By combining ML with molecular dynamics simulations, we reveal that cancer-associated PTEN mutations often cause global destabilization and long-range allosteric perturbations, whereas ASD- and dual-phenotype mutations induce localized dynamic changes in conserved functional regions. Together, these findings highlight the power of combining ML and protein dynamics to bridge genotype–phenotype relationships and to uncover shared molecular mechanisms, thereby informing precision medicine strategies for both cancer and NDDs. References: [1] J Chem Inf Model. 11; 65(15): 8375-8384. [2] J Chem Inf Model. 28; 65(8): 4173-4188.

Chirobiophore: A new paradigm for the multidimensional characterization of chirality of complex biological macromolecular systems

• Claudiu Lungu (University Babes Bolyai, Cluj-Napoca, Romania)

Room: Lacture room 124 Abstract: Chirality is a pervasive and functionally critical property of biological macromolecules, yet distributed and emergent forms of chirality remain poorly quantified in complex systems such as membrane proteins. To address this gap, we introduce Chirobiophore, a multiscale, coordinate-invariant framework for capturing biochirality from atomic geometries to global structural asymmetries. The framework encodes chirality as a six-dimensional vector—comprising Local Tetrahedral Asymmetry (LTA), Helical Path Curvature (HPC), Asymmetric Environment Score (AES), Directional Density Profile (DDP), Leaflet Asymmetry Index (LAI), and Orientation Twist Score (OTS). Applied to canonical α-helical proteins, G protein–coupled receptors, and topologically complex membrane proteins, Chirobiophore reveals distinct chirality signatures that cluster by structural class and functional role. We further show that Chirobiophore descriptors can be projected onto tissue-level asymmetry indices, providing a bridge between molecular structure and morphogenetic patterning. This paradigm offers a unified and extensible platform for structural biology, protein engineering, and developmental modeling of chirality. Reference: 1. Chirality descriptors for structure–activity relationship modeling of bioactive molecules, R. Natarajan, C. N. Lungu, and S. C. Basak, Journal of Mathematical Chemistry https://doi.org/10.1007/s10910-023-01531-2

Applying Machine Learning Techniques to Evaluate DNA-Reactive Properties of Generic Drug Impurities

• Suman Chakravarti (VP of MultiCase inc., Ohio, USA)

Room: Lacture room 124 Abstract: The ICH M7 guideline prioritizes controlling DNA-reactive impurities in pharmaceuticals to reduce carcinogenic risk, yet experimental data for many generic drug impurities remain limited. This talk highlights how machine learning–based (Q)SAR methodologies, combined with expert review, can bridge this gap. Statistical models trained on large curated datasets deliver broad coverage and predictive accuracy, while expert rule–based systems provide mechanistic insight through structural alerts and mitigating features. Used together, these complementary approaches enable systematic ICH M7 classification and informed decision-making. Case studies will demonstrate how this framework offers timely, cost-effective, and scientifically robust evaluations of DNA-reactive properties in generic drug impurities, ultimately supporting safer medicines.

Atomic charges and molecular electrostatic potential in drug design: refined empirical approaches

• Vladimir A. Palyulin (M. V. Lomonosov Moscow State University, Russia)

Room: Lacture room 124 Abstract: The correct reproduction of the molecular electrostatic potential (MEP) is very important for the analysis and account of electrostatic interactions in molecular modeling. The classical force fields for biomolecules and drug-like molecules usually use the atomic point charges to model and describe MEP. The equilibration of atomic (orbital) electronegativities for the computation of partial atomic charges is one of the approaches and it was demonstrated how the formalism of the theory of electrical circuits can be applied for that. However, it has been pointed out in literature that such an approximation is not always enough, and some groups, like amino group or heavy halogens, require the use of anisotropic model for better description of their MEP. At the same time, the formally charged groups have not been as extensively and systematically studied as their neutral counterparts. We have demonstrated that the anisotropic models for formally charged groups do bring improvements in the reference MEP reproduction, that are comparable in magnitude to those for neutral groups. Comparison of the obtained charges with those produced by means of various computational schemes has shown that the developed charge models are well suited for application in many areas of molecular modeling and QSAR/QSPR studies. The relative importance of various electronic effects in change calculation schemes was also estimated. First, the account of formal charges is of primordial importance. Second, the nearest neighbors account is the next in significance. Third, the explicit account of inductive effect in empirical charge calculation schemes was shown to significantly improve the quality of MEP reproduction. Fourth, the contribution of polarization is indirectly assessed. Surprisingly, it is of the order of magnitude of the inductive effect even for the molecular systems, for which it is anticipated to be more significant. Finally, the relative importance of anisotropic effects in neutral molecules was additionally analyzed. A new quadrupole-based approach for halogen bonding description was also tested. It was shown that the suggested electrostatics model built by the addition of fixed atomic quadrupoles to heavy halogen atoms, provides a consistently good description of intermolecular interactions between halogen atom and both hydrogen bond donors and halogen bond acceptors. The solvation free energy differences for F-, Cl-, Br-, and I-substituted benzenes are in excellent agreement with the experimental values. The quadrupoles used were obtained by fitting electrostatic models to an ab initio reference MEP and are independent of the force field. This approach of quadrupole addition should work for any kind of numerical experiment in the field of medicinal chemistry ranging from a direct molecular dynamics investigation of a protein−ligand system to an incorporation into scoring functions and QSAR/QSPR models.

Higher Dimensional Structures for Better Understanding of the Chemical Space and Its Evolution

• Guillermo Restrepo (Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany)

Room: Lacture room 124 Abstract: Abstract: Modelling the evolution of the chemical space, spanned by all chemicals and reactions reported in the literature, requires the development of mathematical and computational frameworks that encode the dynamics of the entire network linking chemicals through chemical reactions. In this presentation, I will explore some of the current computational ontologies for chemistry, their importance and shortcomings, and the need for a stable ontological framework for the future of chemistry and its computational scope. Additionally, I will provide a mathematical framework that relates the various ontologies of chemistry and their interconnections. In the second part of the talk, I will examine the available models for chemical space, focusing particularly on those based on hypergraphs, in contrast to the traditional graph-based approaches. I will discuss the advantages of the hypergraph framework, along with its associated challenges. Some results will be presented on the use of hypergraphs, covering the evolution of the chemical space from 1800 to the present day. I will also address the significance of random hypergraphs and their chemical relevance, as well as recent advances in exploring the mathematics behind these random structures. Lastly, I will highlight the role of generative models for hypergraphs, along with their chemical significance and implications. Keywords: Chemical space, hypergraphs, higher-order structures, chemical ontologies

A Quantum Mechanical Approach to Correlate the Chemical Reactivity Descriptors with Catalytic Reactivity

• Tanmoy Chakraborty

Room: Lacture room 124

Laplacian Matrix Descriptors Based on Atomic Properties

• Igor Kuzmanovski (Institute of Chemistry)

Room: Lacture room 124

Mediation of Electron Transfer by Quadrupolar Interactions: A Characterization of Covalency within van der Waals Distance Limit

• Suman Mallick (Sharda University, India)

Room: Lacture room 124 Abstract: Characterization of covalency of intermolecular interactions in the van der Waals distance limit remains challenging because the interactions between molecules are weak, dynamic, and not measurable. We approach this issue in a series of supramolecular mixed-valence (MV) donor(D)–bridge(B)‒acceptor(A) systems consisting of two bridged Mo2 units with a C6H6 molecule encapsulated, as characterized by the X-ray crystal structures. Herein, the host-guest quadrupolar interaction between the Mo2 integrated D–B–A system and C6H6 molecule shows profound effect on the intramolecular electron transfer. Comparative analysis of the intervalence charge transfer spectra in aromatic and non-aromatic solvents substantiates the strong electronic decoupling effect of the solvating benzene molecule that breaks down the dielectric solvation theory. Ab initio and density functional theory (DFT) calculations indicate that the intermolecular orbital overlaps between the complex bridge and the C6H6 molecule alter the electronic states of the D–B–A molecule through intermolecular nuclear dynamics. This work exemplifies that site-specific intermolecular interaction can be utilized to control the chemical property of supramolecular systems and to elucidate the functionalities of side-chains in biological systems.

Modelling the dynamics of molecular clusters with atom-centered density matrix propagation techniques, coupled with time series analysis and two-dimensional correlation approaches

• Ljupčo Pejov (Ss. Cyril and Methodius University in Skopje, Macedonia)

Room: Lacture room 124