Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Standardized Approach for Diversification of Complex Small Molecules via Aryl Thianthrenium Salts

Thianthrenation is a useful strategy for the late-stage diversification of complex small molecules owing to the positional selectivity and the synthetic versatility of thianthrenium salts as electrophilic linchpins. However, substrate-dependent identification of suitable reaction conditions for thianthrenation can be difficult. Reported reaction conditions for the functionalization of thianthrenium salts vary significantly and, in some instances, lack robustness and practicality. Herein, we report a generalized approach for the preparation of thianthrenium salts and two reaction manifolds for practical, robust, and parallel diversification of thianthrenium salts.

The Theranostic Genome

Theranostic drugs represent an emerging path to deliver on the promise of precision medicine. However, bottlenecks remain in characterizing theranostic targets, identifying theranostic lead compounds, and tailoring theranostic drugs. To overcome these bottlenecks, we present the Theranostic Genome, the part of the human genome whose expression can be utilized to combine therapeutic and diagnostic applications. Using a deep learning-based hybrid human-AI pipeline that cross-references PubMed, the Gene Expression Omnibus, DisGeNET, The Cancer Genome Atlas and the NIH Molecular Imaging and Contrast Agent Database, we bridge individual genes in human cancers with respective theranostic compounds. Cross-referencing the Theranostic Genome with RNAseq data from over 17'000 human tissues identifies theranostic targets and lead compounds for various human cancers, and allows tailoring targeted theranostics to relevant cancer subpopulations. We expect the Theranostic Genome to facilitate the development of new targeted theranostics to better diagnose, understand, treat, and monitor a variety of human cancers.

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry

This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the m/z value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery.

We are MedChem: The Frontiers in Medicinal Chemistry 2024

The Frontiers in Medicinal Chemistry (FiMC) is the largest international Medicinal Chemistry conference in Germany and took place from March 17th to 20th 2024 in Munich. Co-organized by the Division of Medicinal Chemistry of the German Chemical Society (Gesellschaft Deutscher Chemiker; GDCh) and the Division of Pharmaceutical and Medicinal Chemistry of the German Pharmaceutical Society (Deutsche Pharmazeutische Gesellschaft; DPhG), and supported by a local organizing committee from the Ludwigs-Maximilians-University Munich headed by Daniel Merk, the meeting brought together approximately 225 participants from 20 countries. The outstanding program of the four-day conference included 40 lectures by leading scientists from industry and academia as well as early career investigators. Moreover, 100 posters were presented in two highly interactive poster sessions.

Drugs from drugs: New chemical insights into a mature concept

Developing new drugs from marketed ones is a well-established and successful approach in drug discovery. We offer a unified view of this field, focusing on the new chemical aspects of the involved approaches: (a) chemical transformation of the original drugs (late-stage modifications, molecular editing), (b) prodrug strategies, and (c) repurposing as a tool to develop new hits/leads. Special focus is placed on the molecular structure of the drugs and their synthetic feasibility. The combination of experimental advances and new computational approaches, including artificial intelligence methods, paves the way for the evolution of the drugs from drugs concept.

Combining de novo molecular design with semiempirical protein-ligand binding free energy calculation

Semi-empirical quantum chemistry methods estimate the binding free energies of protein-ligand complexes. We present an integrated approach combining the GFN2-xTB method with de novo design for the generation and evaluation of potential inhibitors of acetylcholinesterase (AChE). We employed chemical language model-based molecule generation to explore the synthetically accessible chemical space around the natural product Huperzine A, a potent AChE inhibitor. Four distinct molecular libraries were created using structure- and ligand-based molecular de novo design with SMILES and SELFIES representations, respectively. These libraries were computationally evaluated for synthesizability, novelty, and predicted biological activity. The candidate molecules were subjected to molecular docking to identify hypothetical binding poses, which were further refined using Gibbs free energy calculations. The structurally novel top-ranked molecule was chemically synthesized and biologically tested, demonstrating moderate micromolar activity against AChE. Our findings highlight the potential and certain limitations of integrating deep learning-based molecular generation with semi-empirical quantum chemistry-based activity prediction for structure-based drug design.

Reinforcement Learning for Improving Chemical Reaction Performance

Deep learning (DL) methods have gained notable prominence in predictive and generative tasks in molecular space. However, their application in chemical reactions remains grossly underutilized. Chemical reactions are intrinsically complex: typically involving multiple molecules besides bond-breaking/forming events. In reaction discovery, one aims to maximize yield and/or selectivity that depends on a number of factors, mostly centered on reacting partners and reaction conditions. Herein, we introduce RE-EXPLORE, a novel approach that integrates deep reinforcement learning (RL) with an RNN-based deep generative model to identify prospective new reactants/catalysts, whose yield/selectivity is estimated using a pretrained regressor. Three chemical databases (ChEMBL, ZINC, and COCONUT containing half a million to one million unlabeled molecules) are independently used for pretraining the generators to enrich them with valuable information from diverse chemical space. Standard RL methods are found to be insufficient, as learners tend to prioritize exploitation for immediate gains, resulting in repetitive generation of same/similar molecules. Our engineered reward function includes a Tanimoto-based uniqueness factor within the RL loop that improved the exploration of the environment and has helped accrue larger returns. Integration of a user-defined core fragment into the generated molecules facilitated learning of specific reaction types. Together, RE-EXPLORE can navigate the reaction space toward practically meaningful regions and offers notable improvements across the three distinct reaction types considered in this study. It identifies high-yielding substrates and highly enantioselective chiral catalysts. This RL-based approach has the potential to expedite reaction discovery and aid in the synthesis planning of important compounds, including drugs and pharmaceuticals.

Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions

Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain.

Meeting report of the 5th European Biotransformation Workshop

Challenges, strategies and new technologies in the field of biotransformation were presented and discussed at the 5th European Biotransformation Workshop, which was held on March 14, 2024 on the Novartis Campus in Basel, Switzerland.In this meeting report we summarise the presentations and discussions from this workshop.The topics covered are listed below:Advances in understanding drug induced liver injury (DILI) risks of carboxylic acids and targeted covalent inhibitors.Biotransformation of oligonucleotide-based therapeutics including automated software tools for metabolite identification.Recent advances in metabolite synthesisQualification and validation of a new compact Low Energy Accelerator Mass Spectrometry (LEA) system for metabolite profiling.

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry

Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.

Highlights from the 57th Bürgenstock Conference on Stereochemistry 2024

Herein, we share an overview of the scientific highlights from speakers at the latest edition of the longstanding Bürgenstock Conference.

Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity

C-H borylation is a high-value transformation in the synthesis of lead candidates for the pharmaceutical industry because a wide array of downstream coupling reactions is available. However, predicting its regioselectivity, especially in drug-like molecules that may contain multiple heterocycles, is not a trivial task. Using a data set of borylation reactions from Reaxys, we explored how a language model originally trained on USPTO_500_MT, a broad-scope set of patent data, can be used to predict the C-H borylation reaction product in different modes: product generation and site reactivity classification. Our fine-tuned T5Chem multitask language model can generate the correct product in 79% of cases. It can also classify the reactive aromatic C-H bonds with 95% accuracy and 88% positive predictive value, exceeding purpose-developed graph-based neural networks.

Prospective de novo drug design with deep interactome learning

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.