Task-similarity is a crucial factor for few-shot meta-learning of structure-activity relationships.

Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process.

Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning.

Lead optimization supported by artificial intelligence (AI)-based generative models has become increasingly important in drug design. Success factors are reagent availability, novelty, and the optimization of multiple properties. Directed fragment-replacement is particularly attractive, as it mimics medicinal chemistry tactics. Here, we present variations of fragment-based reinforcement learning using an actor-critic model. Novel features include freezing fragments and using reagents as the fragment source. Splitting molecules according to reaction schemes improves synthesizability, while tuning network output probabilities allows us to balance novelty versus diversity. Combining fragment-based optimization with virtual library encodings allows the exploration of large chemical spaces with synthesizable ideas. Collectively, these enhancements influence design toward high-quality molecules with favorable profiles. A validation study using 15 pharmaceutically relevant targets reveals that novel structures are obtained for most cases, which are identical or related to independent validation sets for each target. Hence, these modifications significantly increase the value of fragment-based reinforcement learning for drug design. The code is available on GitHub: https://github.com/Sanofi-Public/IDD-papers-fragrl.

Back in Person: Frontiers in Medicinal Chemistry 2023.

The Frontiers in Medicinal Chemistry (FiMC) is the largest international Medicinal Chemistry conference in the German speaking area and took place from April 3rd to 5th 2023 in Vienna (Austria). Fortunately, after being cancelled in 2020 and two years (2021-2022) of entirely virtual meetings, due to the COVID-19 pandemic, the FiMC could be held in a face-to-face format again. Organized by the Division of Medicinal Chemistry of the German Chemical Society (GDCh), the Division of Pharmaceutical and Medicinal Chemistry of the German Pharmaceutical Society (DPhG), together with the Division of Medicinal Chemistry of the Austrian Chemical Society (GÖCH), the Austrian Pharmaceutical Society (ÖPhG), and a local organization committee from the University of Vienna headed by Thierry Langer, the meeting brought together 260 participants from 21 countries. The program included 38 lectures by leading scientists from industry and academia as well as early career investigators. Moreover, 102 posters were presented in two highly interactive poster sessions.

Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods.

The identification and optimization of promising lead molecules is essential for drug discovery. Recently, artificial intelligence (AI) based generative methods provided complementary approaches for generating molecules under specific design constraints of relevance in drug design. The goal of our study is to incorporate protein 3D information directly into generative design by flexible docking plus an adapted protein-ligand scoring function, thereby moving towards automated structure-based design. First, the protein-ligand scoring function RFXscore integrating individual scoring terms, ligand descriptors, and combined terms was derived using the PDBbind database and internal data. Next, design results for different workflows are compared to solely ligand-based reward schemes. Our newly proposed, optimal workflow for structure-based generative design is shown to produce promising results, especially for those exploration scenarios, where diverse structures fitting to a protein binding site are requested. Best results are obtained using docking followed by RFXscore, while, depending on the exact application scenario, it was also found useful to combine this approach with other metrics that bias structure generation into "drug-like" chemical space, such as target-activity machine learning models, respectively.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence.

In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important for activity. However, this information is crucial for capturing the underlying structure-activity relationships (SARs) to guide further optimization. To address this interpretation gap, "Explainable Artificial Intelligence" (XAI) methods recently became popular. Herein, we apply and compare multiple XAI methods to projects of lead optimization data sets with well-established SARs and available X-ray crystal structures. As we can show, easily understandable and comprehensive interpretations are obtained by combining DNN models with some powerful interpretation methods. In particular, SHAP-based methods are promising for this task. A novel visualization scheme using atom-based heatmaps provides useful insights into the underlying SAR. It is important to note that all interpretations are only meaningful in the context of the underlying models and associated data.

Artificial Intelligence in Compound Design.

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.

Application of Deep Neural Network Models in Drug Discovery Programs.

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery.

The PROTACtable genome.

Proteolysis-targeting chimeras (PROTACs) are an emerging drug modality that may offer new opportunities to circumvent some of the limitations associated with traditional small-molecule therapeutics. By analogy with the concept of the 'druggable genome', the question arises as to which potential drug targets might PROTAC-mediated protein degradation be most applicable. Here, we present a systematic approach to the assessment of the PROTAC tractability (PROTACtability) of protein targets using a series of criteria based on data and information from a diverse range of relevant publicly available resources. Our approach could support decision-making on whether or not a particular target may be amenable to modulation using a PROTAC. Using our approach, we identified 1,067 proteins of the human proteome that have not yet been described in the literature as PROTAC targets that offer potential opportunities for future PROTAC-based efforts.

A Solid Supported Membrane-Based Technology for Electrophysical Screening of B0AT1-Modulating Compounds.

Classical high-throughput screening (HTS) technologies for the analysis of ionic currents across biological membranes can be performed using fluorescence-based, radioactive, and mass spectrometry (MS)-based uptake assays. These assays provide rapid results for pharmacological HTS, but the underlying, indirect analytical character of these assays can be linked to high false-positive hit rates. Thus, orthogonal and secondary assays using more biological target-based technologies are indispensable for further compound validation and optimization. Direct assay technologies for transporter proteins are electrophysiology-based, but are also complex, time-consuming, and not well applicable for automated profiling purposes. In contrast to conventional patch clamp systems, solid supported membrane (SSM)-based electrophysiology is a sensitive, membrane-based method for transporter analysis, and current technical developments target the demand for automated, accelerated, and sensitive assays for transporter-directed compound screening. In this study, the suitability of the SSM-based technique for pharmacological compound identification and optimization was evaluated performing cell-free SSM-based measurements with the electrogenic amino acid transporter B0AT1 (SLC6A19). Electrophysiological characterization of leucine-induced currents demonstrated that the observed signals were specific to B0AT1. Moreover, B0AT1-dependent responses were successfully inhibited using an established in-house tool compound. Evaluation of current stability and data reproducibility verified the robustness and reliability of the applied assay. Active compounds from primary screens of large compound libraries were validated, and false-positive hits were identified. These results clearly demonstrate the suitability of the SSM-based technique as a direct electrophysiological method for rapid and automated identification of small molecules that can inhibit B0AT1 activity.

Quantum-mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Joint academic-industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein-ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum-mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum-mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol-water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia-industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.

Using Graph Databases to Investigate Trends in Structure-Activity Relationship Networks.

Mining the steadily increasing amount of chemical and biological data is a key challenge in drug discovery. Graph databases offer viable alternatives for capturing interrelationships between molecules and for generating novel insights for design. In a graph database, molecules and their properties are mapped to nodes, while relationships are described by edges. Here, we introduce a graph database for navigation in chemical space, analogue searching, and structure-activity relationship (SAR) analysis. We illustrate this concept using hERG channel inhibitors from ChEMBL to extract SAR knowledge. This graph database is built using different relationships, namely 2D-fingerprint similarity, matched molecular pairs, topomer distances, and structure-activity landscape indices (SALI). Typical applications include retrieving analogues linked by single or multiple edge paths to the query compound as well as detection of nonadditive SAR features. Finally, we identify triplets of linked molecules for clustering. The speed of searching and analysis allows the user to interactively navigate the database and to address complex questions in real-time.

Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins.

Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.

Automated De Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn?

Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations.

Relative binding free energy (RBFE) prediction methods such as free energy perturbation (FEP) are important today for estimating protein-ligand binding affinities. Significant hardware and algorithmic improvements now allow for simulating congeneric series within days. Therefore, RBFE calculations have an enormous potential for structure-based drug discovery. As typically only a few representative crystal structures for a series are available, other ligands and design proposals must be reliably superimposed for meaningful results. An observed significant effect of the alignment on FEP led us to develop an alignment approach combining docking with maximum common substructure (MCS) derived core constraints from the most similar reference pose, named MCS-docking workflow. We then studied the effect of binding pose generation on the accuracy of RBFE predictions using six ligand series from five pharmaceutically relevant protein targets. Overall, the MCS-docking workflow generated consistent poses for most of the ligands in the investigated series. While multiple alignment methods often resulted in comparable FEP predictions, for most of the cases herein, the MCS-docking workflow produced the best accuracy in predictions. Furthermore, the FEP analysis data strongly support the hypothesis that the accuracy of RBFE predictions depends on input poses to construct the perturbation map. Therefore, an automated workflow without manual intervention minimizes potential errors and obtains the most useful predictions with significant impact for structure-based design.

From identification to functional characterization of cyriotoxin-1a, an antinociceptive toxin from the spider Cyriopagopus schioedtei.

BACKGROUND AND PURPOSE: The NaV 1.7 channel is highly expressed in dorsal root ganglia of the sensory nervous system and plays a central role in the pain signalling process. We investigated a library prepared from original venoms of 117 different animals to identify new selective inhibitors of this target. EXPERIMENTAL APPROACH: We used high throughput screening of a large venom collection using automated patch-clamp experiments on human voltage-gated sodium channel subtypes and then in vitro and in vivo electrophysiological experiments to characterize the active peptides that have been purified, sequenced, and chemically synthesized. Analgesic effects were evaluated in vivo in mice models. KEY RESULTS: We identified cyriotoxin-1a (CyrTx-1a), a novel peptide isolated from Cyriopagopus schioedtei spider venom, as a candidate for further characterization. This 33 amino acids toxin belongs to the inhibitor cystine knot structural family and inhibits hNaV 1.1-1.3 and 1.6-1.7 channels in the low nanomolar range, compared to the micromolar range for hNaV 1.4-1.5 and 1.8 channels. CyrTx-1a was 920 times more efficient at inhibiting tetrodotoxin (TTX)-sensitive than TTX-resistant sodium currents recorded from adult mouse dorsal root ganglia neurons and in vivo electrophysiological experiments showed that CyrTx-1a was approximately 170 times less efficient than huwentoxin-IV at altering mouse skeletal neuromuscular excitability properties. CyrTx-1a exhibited an analgesic effect in mice by increasing reaction time in the hot-plate assay. CONCLUSIONS AND IMPLICATIONS: The pharmacological profile of CyrTx-1a paves the way for further molecular engineering aimed to optimize the potential antinociceptive properties of this peptide.

Peptide Optimization at the Drug Discovery-Development Interface: Tailoring of Physicochemical Properties Toward Specific Formulation Requirements.

Physicochemical properties of peptides need to be compatible with the manufacturing process and formulation requirements to ensure developability toward the commercial drug product. This aspect is often disregarded and only evaluated late in discovery, imposing a high risk for delays in development, increased costs, and finally for the project in general. Here, we report a case study of early physicochemical peptide characterization and optimization of dual glucagon-like peptide 1/glucagon receptor agonists toward specific formulation requirements. Aggregation issues which were observed at acidic pH in the presence of phenolic preservatives could be eliminated by modification of the peptide sequence, and chemical stability issues were significantly improved by addition of stabilizing formulation excipients. We describe structural, analytical, and biophysical characterization in different compositions to analyze the effect of pH and formulation excipients on physical and chemical stability. Molecular models have been generated to rationalize peptide stability behavior based on computed physicochemical descriptors and interactions with excipients. To conclude these studies, a general roadmap is proposed how to assess and optimize early physicochemical peptide properties in a sophisticated way by combining experimental and in silico profiling to provide stable peptide drugs under relevant formulation conditions at the end of discovery.

The Role of Bridging Water and Hydrogen Bonding as Key Determinants of Noncovalent Protein-Carbohydrate Recognition.

Mechanisms of protein-carbohydrate recognition attract a lot of interest due to their roles in various cellular processes and metabolism disorders. We have performed a large-scale analysis of protein structures solved in complex with glucose, galactose and their substituted analogues. We found that, on average, sugar molecules establish five hydrogen bonds (HBs) in the binding site, including one to three HBs with bridging water molecules. The free energy contribution of bridging and direct HBs was estimated using the free energy perturbation (FEP+) methodology for mono- and disaccharides that bind to l-ABP, ttGBP, TrmB, hGalectin-1 and hGalectin-3. We show that removing hydroxy groups that are engaged in direct HBs with the charged groups of Asp, Arg and Glu residues, protein backbone amide or buried water dramatically decreases binding affinity. In contrast, all solvent-exposed hydroxy groups and hydroxy groups engaged in HBs with the solvent-exposed bridging water molecules contribute weakly to binding affinity and so can be replaced to optimize ligand potency. Finally, we rationalize an effect of binding site water replacement on the binding affinity to l-ABP.

Artificial Intelligence in Drug Design.

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.

The hpCADD NDDO Hamiltonian: Parametrization.

A neglect of diatomic differential overlap (NDDO) Hamiltonian has been parametrized as an electronic component of a polarizable force field. Coulomb and exchange potentials derived directly from the NDDO Hamiltonian in principle can be used with classical potentials, thus forming the basis for a new generation of efficiently applicable multipolar polarizable force fields. The new hpCADD Hamiltonian uses force-field-like atom types and reproduces the electrostatic properties (dipole moment, molecular electrostatic potential) and Koopmans' theorem ionization potentials closely, as demonstrated for a large training set and an independent test set of small molecules. The Hamiltonian is not intended to reproduce geometries or total energies well, as these will be controlled by the classical force-field potentials. In order to establish the hpCADD Hamiltonian as an electronic component in force-field-based calculations, we tested its performance in combination with the 3D reference interaction site model (3D RISM) for aqueous solutions. Comparison of the resulting solvation free energies for the training and test sets to atomic charges derived from standard procedures, exact solute-solvent electrostatics based on high-level quantum-chemical reference data, and established semiempirical Hamiltonians demonstrates the advantages of the hpCADD parametrization.