Ultra-large library docking for discovering new chemotypes

Drug Discovery in the Field of β-Lactams: An Academic Perspective

β-Lactams are the most widely prescribed class of antibiotics that inhibit penicillin-binding proteins (PBPs), particularly transpeptidases that function in peptidoglycan synthesis. A major mechanism of antibiotic resistance is the production of β-lactamase enzymes, which are capable of hydrolyzing β-lactam antibiotics. There have been many efforts to counter increasing bacterial resistance against β-lactams. These studies have mainly focused on three areas: discovering novel inhibitors against β-lactamases, developing new β-lactams less susceptible to existing resistance mechanisms, and identifying non-β-lactam inhibitors against cell wall transpeptidases. Drug discovery in the β-lactam field has afforded a range of research opportunities for academia. In this review, we summarize the recent new findings on both β-lactamases and cell wall transpeptidases because these two groups of enzymes are evolutionarily and functionally connected. Many efforts to develop new β-lactams have aimed to inhibit both transpeptidases and β-lactamases, while several promising novel β-lactamase inhibitors have shown the potential to be further developed into transpeptidase inhibitors. In addition, the drug discovery progress against each group of enzymes is presented in three aspects: understanding the targets, screening methodology, and new inhibitor chemotypes. This is to offer insights into not only the advancement in this field but also the challenges, opportunities, and resources for future research. In particular, cyclic boronate compounds are now capable of inhibiting all classes of β-lactamases, while the diazabicyclooctane (DBO) series of small molecules has led to not only new β-lactamase inhibitors but potentially a new class of antibiotics by directly targeting PBPs. With the cautiously optimistic successes of a number of new β-lactamase inhibitor chemotypes and many questions remaining to be answered about the structure and function of cell wall transpeptidases, non-β-lactam transpeptidase inhibitors may usher in the next exciting phase of drug discovery in this field.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking

To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Exploring Superacid-Promoted Skeletal Reorganization of Aliphatic Nitrogen-Containing Compounds

Here we report a method to reorganize the core structure of aliphatic unsaturated nitrogen-containing substrates exploiting polyprotonation in superacid solutions. The superelectrophilic activation of N-isopropyl systems allows for the selective formal Csp3 -H activation/cyclization or homologation / functionalization of nitrogen-containing substrates. This study also reveals that this skeletal reorganization can be controlled through protonation interplay. The mechanism of this process involves an original sequence of C-N bond cleavage, isopropyl cation generation and subsequent C-N bond and C-C bond formation. This was demonstrated through in situ NMR analysis and labelling experiments, also confirmed by DFT calculations.

The rise of epitranscriptomics: recent developments and future directions

The epitranscriptomics field has undergone tremendous growth since the discovery that the RNA N6-methyladenosine (m6A) modification is reversible and is distributed throughout the transcriptome. Efforts to map RNA modifications transcriptome-wide and reshape the epitranscriptome in disease settings have facilitated mechanistic understanding and drug discovery in the field. In this review we discuss recent advancements in RNA modification detection methods and consider how these developments can be applied to gain novel insights into the epitranscriptome. We also highlight drug discovery efforts aimed at developing epitranscriptomic therapeutics for cancer and other diseases. Finally, we consider engineering of the epitranscriptome as an emerging direction to investigate RNA modifications and their causal effects on RNA processing at high specificity.

Discovery of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as SARS-CoV-2 main protease inhibitors through virtual screening and biological evaluation

The COVID-19 caused by SARS-CoV-2 has led to a global pandemic that continues to impact societies and economies worldwide. The main protease (Mpro) plays a crucial role in SARS-CoV-2 replication and is an attractive target for anti-SARS-CoV-2 drug discovery. Herein, we report a series of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as non-peptidomimetic inhibitors targeting SARS-CoV-2 Mpro through structure-based virtual screening and biological evaluation. Further similarity search and structure-activity relationship study led to the identification of compound M56-S2 with the enzymatic IC50 value of 4.0 μM. Moreover, the molecular simulation and predicted ADMET properties, indicated that non-peptidomimetic inhibitor M56-S2 might serve as a useful starting point for the further discovery of highly potent inhibitors targeting SARS-CoV-2 Mpro.

Phytochemicals Showing Antiangiogenic Effect in Pre-clinical Models and their Potential as an Alternative to Existing Therapeutics

Angiogenesis, the formation of new blood vessels from a pre-existing vascular network, is an important hallmark of several pathological conditions, such as tumor growth and metastasis, proliferative retinopathies, including proliferative diabetic retinopathy and retinopathy of prematurity, age-related macular degeneration, rheumatoid arthritis, psoriasis, and endometriosis. Putting a halt to pathology-driven angiogenesis is considered an important therapeutic strategy to slow down or reduce the severity of pathological disorders. Considering the attrition rate of synthetic antiangiogenic compounds from the lab to reaching the market due to severe side effects, several compounds of natural origin are being explored for their antiangiogenic properties. Employing pre-clinical models for the evaluation of novel antiangiogenic compounds is a promising strategy for rapid screening of antiangiogenic compounds. These studies use a spectrum of angiogenic model systems that include HUVEC two-dimensional culture, nude mice, chick chorioallantoic membrane, transgenic zebrafish, and dorsal aorta from rats and chicks, depending upon available resources. The present article emphasizes the antiangiogenic activity of the phytochemicals shown to exhibit antiangiogenic behavior in these well-defined existing angiogenic models and highlights key molecular targets. Different models help to get a quick understanding of the efficacy and therapeutics mechanism of emerging lead molecules. The inherent variability in assays and corresponding different phytochemicals tested in each study prevent their immediate utilization in clinical studies. This review will discuss phytochemicals discovered using suitable preclinical antiangiogenic models, along with a special mention of leads that have entered clinical evaluation.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.

Recent advances in anti-inflammatory active components and action mechanisms of natural medicines

Inflammation is a series of reactions caused by the body's resistance to external biological stimuli. Inflammation affects the occurrence and development of many diseases. Anti-inflammatory drugs have been used widely to treat inflammatory diseases, but long-term use can cause toxic side-effects and affect human functions. As immunomodulators with long-term conditioning effects and no drug residues, natural products are being investigated increasingly for the treatment of inflammatory diseases. In this review, we focus on the inflammatory process and cellular mechanisms in the development of diseases such as inflammatory bowel disease, atherosclerosis, and coronavirus disease-2019. Also, we focus on three signaling pathways (Nuclear factor-kappa B, p38 mitogen-activated protein kinase, Janus kinase/signal transducer and activator of transcription-3) to explain the anti-inflammatory effect of natural products. In addition, we also classified common natural products based on secondary metabolites and explained the association between current bidirectional prediction progress of natural product targets and inflammatory diseases.

Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains

P-glycoprotein (P-gp), a membrane transport protein overexpressed in certain drug-resistant cancer cells, has been the target of numerous drug discovery projects aimed at overcoming drug resistance in cancer. Most characterized P-gp inhibitors bind at the large hydrophobic drug binding domain (DBD), but none have yet attained regulatory approval. In this study, we explored the potential of designing inhibitors that target the nucleotide binding domains (NBDs), by computationally screening a large library of 2.6 billion synthesizable molecules, using a combination of machine learning-guided molecular docking and molecular dynamics (MD). 14 of the computationally best-scoring molecules were subsequently tested for their ability to inhibit P-gp mediated calcein-AM efflux. In total, five diverse compounds exhibited inhibitory effects in the calcein-AM assay without displaying toxicity. The activity of these compounds was confirmed by their ability to decrease the verapamil-stimulated ATPase activity of P-gp in a subsequent assay. The discovery of these five novel P-gp inhibitors demonstrates the potential of in-silico screening in drug discovery and provides a new stepping point towards future potent P-gp inhibitors.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections

Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.

Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines

The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

Molecular basis of opioid receptor signaling

Opioids are used for pain management despite the side effects that contribute to the opioid crisis. The pursuit of non-addictive opioid analgesics remains unattained due to the unresolved intricacies of opioid actions, receptor signaling cascades, and neuronal plasticity. Advancements in structural, molecular, and computational tools illuminate the dynamic interplay between opioids and opioid receptors, as well as the molecular determinants of signaling pathways, which are potentially interlinked with pharmacological responses. Here, we review the molecular basis of opioid receptor signaling with a focus on the structures of opioid receptors bound to endogenous peptides or pharmacological agents. These insights unveil specific interactions that dictate ligand selectivity and likely their distinctive pharmacological profiles. Biochemical analysis further unveils molecular features governing opioid receptor signaling. Simultaneously, the synergy between computational biology and medicinal chemistry continues to expedite the discovery of novel chemotypes with the promise of yielding more efficacious and safer opioid compounds.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Deep Learning with Geometry-Enhanced Molecular Representation for Augmentation of Large-Scale Docking-Based Virtual Screening

Structure-based virtual screening has been a crucial tool in drug discovery for decades. However, as the chemical space expands, the existing structure-based virtual screening techniques based on molecular docking and scoring struggle to handle billion-entry ultralarge libraries due to the high computational cost. To address this challenge, people have resorted to machine learning techniques to enhance structure-based virtual screening for efficiently exploring the vast chemical space. In those cases, compounds are usually treated as sequential strings or two-dimensional topology graphs, limiting their ability to incorporate three-dimensional structural information for downstream tasks. We herein propose a novel deep learning protocol, GEM-Screen, which utilizes the geometry-enhanced molecular representation of the compounds docking to a specific target and is trained on docking scores of a small fraction of a library through an active learning strategy to approximate the docking outcome for yet nontraining entries. This protocol is applied to virtual screening campaigns against the AmpC and D4 targets, demonstrating that GEM-Screen enriches more than 90% of the hit scaffolds for AmpC in the top 4% of model predictions and more than 80% of the hit scaffolds for D4 in the same top-ranking size of library. GEM-Screen can be used in conjunction with traditional docking programs for docking of only the top-ranked compounds to avoid the exhaustive docking of the whole library, thus allowing for discovering top-scoring compounds from billion-entry libraries in a rapid yet accurate fashion.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

High-throughput screening of ancient forest plant extracts shows cytotoxicity towards triple-negative breast cancer

According to the World Health Organization, women's breast cancer is among the most common cancers with 7.8 million diagnosed cases during 2016-2020 and encompasses 15 % of all female cancer-related mortalities. These mortality events from triple-negative breast cancer are a significant health issue worldwide calling for a continuous search of bioactive compounds for better cancer treatments. Historically, plants are important sources for identifying such new bioactive chemicals for treatments. Here we use high-throughput screening and mass spectrometry analyses of extracts from 100 plant species collected in Chinese ancient forests to detect novel bioactive breast cancer phytochemicals. First, to study the effects on viability of the plant extracts, we used a MTT and CCK-8 cytotoxicity assay employing triple-negative breast cancer (TNBC) MDA-MB-231 and normal epithelial MCF-10A cell lines and cell cycle arrest to estimate apoptosis using flow cytometry for the most potent three speices. Based on these analyses, the final most potent extracts were from the Amur honeysuckle (Lonicera maackii) wood/root bark and Nigaki (Picrasma quassioides) wood/root bark. Then, 5 × 106 MDA-MB-231 cells were injected subcutaneously into the right hind leg of nude mice and a tumour was allowed to grow before treatment for seven days. Subsequently, the four exposed groups received gavage extracts from Amur honeysuckle and Nigaki (Amur honeysuckle wood distilled water, Amur honeysuckle root bark ethanol, Nigaki wood ethanol or Nigaki root bark distilled water/ethanol (1:1) extracts) in phosphate-buffered saline (PBS), while the control group received only PBS. The tumour weight of treated nude mice was reduced significantly by 60.5 % within 2 weeks, while on average killing 70 % of the MDA-MB-231 breast cancer cells after 48 h treatment (MTT test). In addition, screening of target genes using the Swiss Target Prediction, STITCH, STRING and NCBI-gene database showed that the four plant extracts possess desirable activity towards several known breast cancer genes. This reflects that the extracts may kill MBD-MB-231 breast cancer cells. This is the first screening of plant extracts with high efficiency in 2 decades, showing promising results for future development of novel cancer treatments.

EasyDock: customizable and scalable docking tool

Docking of large compound collections becomes an important procedure to discover new chemical entities. Screening of large sets of compounds may also occur in de novo design projects guided by molecular docking. To facilitate these processes, there is a need for automated tools capable of efficiently docking a large number of molecules using multiple computational nodes within a reasonable timeframe. These tools should also allow for easy integration of new docking programs and provide a user-friendly program interface to support the development of further approaches utilizing docking as a foundation. Currently available tools have certain limitations, such as lacking a convenient program interface or lacking support for distributed computations. In response to these limitations, we have developed a module called EasyDock. It can be deployed over a network of computational nodes using the Dask library, without requiring a specific cluster scheduler. Furthermore, we have proposed and implemented a simple model that predicts the runtime of docking experiments and applied it to minimize overall docking time. The current version of EasyDock supports popular docking programs, namely Autodock Vina, gnina, and smina. Additionally, we implemented a supplementary feature to enable docking of boron-containing compounds, which are not inherently supported by Vina and smina, and demonstrated its applicability on a set of 55 PDB protein-ligand complexes.

A practical guide to machine-learning scoring for structure-based virtual screening

Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.

Docking for molecules that bind in a symmetric stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

SIGNIFICANCE

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Functional screening and rational design of compounds targeting GPR132 to treat diabetes

Chronic inflammation due to islet-residing macrophages plays key roles in the development of type 2 diabetes mellitus. By systematically profiling intra-islet lipid-transmembrane receptor signalling in islet-resident macrophages, we identified endogenous 9(S)-hydroxy-10,12-octadecadienoic acid-G-protein-coupled receptor 132 (GPR132)-Gi signalling as a significant contributor to islet macrophage reprogramming and found that GPR132 deficiency in macrophages reversed metabolic disorders in mice fed a high-fat diet. The cryo-electron microscopy structures of GPR132 bound with two endogenous agonists, N-palmitoylglycine and 9(S)-hydroxy-10,12-octadecadienoic acid, enabled us to rationally design both GPR132 agonists and antagonists with high potency and selectivity through stepwise translational approaches. We ultimately identified a selective GPR132 antagonist, NOX-6-18, that modulates macrophage reprogramming within pancreatic islets, decreases weight gain and enhances glucose metabolism in mice fed a high-fat diet. Our study not only illustrates that intra-islet lipid signalling contributes to islet macrophage reprogramming but also provides a broadly applicable strategy for the identification of important G-protein-coupled receptor targets in pathophysiological processes, followed by the rational design of therapeutic leads for refractory diseases such as diabetes.

Fake it until you make it? Generative de novo design and virtual screening of synthesizable molecules

Computational techniques, including virtual screening, de novo design, and generative models, play an increasing role in expediting DMTA cycles for modern molecular discovery. However, computationally proposed molecules must be synthetically feasible for laboratory testing. In this perspective, we offer a succinct introduction to the subject, and showcase typical workflows to integrate synthesis planning, synthesizability scoring, and molecule generation. Finally, we address limitations and opportunities for future research.

ViTScore: A Novel Three-Dimensional Vision Transformer Method for Accurate Prediction of Protein-Ligand Docking Poses

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery, and due to the complexity and high cost of experimental methods, there is a great demand for computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most challenging aspects of protein-ligand docking is to identify near-native conformations from a set of poses, but traditional scoring functions still have limited accuracy. Therefore, new scoring methods are urgently needed for methodological and/or practical implications. We present a novel deep learning-based scoring function for ranking protein-ligand docking poses based on Vision Transformer (ViT), named ViTScore. To recognize near-native poses from a set of poses, ViTScore voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. This allows ViTScore to capture the subtle differences between spatially and energetically favorable near-native poses and unfavorable non-native poses without needing extra information. After that, ViTScore will output the prediction of the root mean square deviation (rmsd) of a docking pose with reference to the native binding pose. ViTScore is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power. Moreover, the results demonstrate that ViTScore is a promising scoring function for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Furthermore, the results suggest that ViTScore is a powerful tool for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Additionally, ViTScore can be used to identify potential drug targets and to design new drugs with improved efficacy and safety.

Identification of potential inhibitors against E.coli via novel approaches based on deep learning and quantum mechanics-based atomistic investigations

Currently, drug resistance to commercially available antibiotics is imparting negative consequences to global health, and the development of novel antibiotics in a timely manner is a prime need of the hour. In the current study, an e-pharmacophore model was built using the 3D structure of DNA gyrase in complex with a standard inhibitor. The generated model was subjected to a pharmacophore based virtual screening against 45,257,086 molecules having 223,460,579 conformers available in MCULE database. Pharmacophore based screening retrieved eight molecules as top hit based on pharmacophoric features in comparison to standard inhibitors. Afterward, all eight compounds were subjected molecular docking based on deep learning algorithm. The molecular docking revealed that compound MCULE-6042843173 and MCULE-2362244223 had significant binding orientation inside active pocket of targeted protein with binding affinity of -9.52 and -9.24 kcal/mol respectively. In addition, density functional theory studies (DFT) were performed to evaluate quantum mechanics of top ranked compounds which were investigated through quantum mechanics (QM) computations which strongly assisted the findings of other in-silico investigations. Consequently, the MCULE-6042843173 and MCULE-2362244223 were subjected to MD simulation studies for evaluation of stability, hydrogen bond analysis, van der Waals interactions, and the contact profile of compounds with targeted amino acid residues. Findings of current study suggested MCULE-6042843173 and MCULE-2362244223 as potential and novel inhibitor of DNA Gyrase enzyme.

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.

Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.

SMiPoly: Generation of a Synthesizable Polymer Virtual Library Using Rule-Based Polymerization Reactions

Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, the difficulty of identifying synthetic routes to such designed polymers remains unresolved. To address this technical limitation, we present Small Molecules into Polymers (SMiPoly), a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48 and 53%, respectively. Incorporating the SMiPoly library into a molecular design workflow will accelerate the process of de novo polymer synthesis by shortening the step to select synthesizable candidate polymers.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.

D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening

Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify. Novel therapeutic targets are often hard-to-drug proteins, such as messengers or transcription factors. Computational strategies arise as a promising solution to expedite drug discovery for unconventional therapeutic targets. FRASE-bot exploits big data and machine learning (ML) to distill 3D information relevant to the target protein from thousands of protein-ligand complexes to seed it with ligand fragments. The seeded fragments can then inform either (i) de novo design of 3D ligand structures or (ii) ultra-large-scale virtual screening of commercially available compounds. Here, FRASE-bot was applied to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising but ligand-orphan drug target implicated in triple negative breast cancer. The signaling function of CIB1 relies on protein-protein interactions and its structure does not feature any natural ligand-binding pocket. FRASE-based virtual screening identified the first small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depleted cells.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Applications and Potential of In Silico Approaches for Psychedelic Chemistry

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

Achieving the promise and avoiding the peril of chemical probes using genetics

Chemical probes can be valuable tools for studying protein targets, but addressing concerns about a probe's cellular target or its specificity can be challenging. A reliable strategy is to use a mutation that does not alter a target's function but confers resistance (or sensitizes) to the inhibitor in both cellular and biochemical assays. However, challenges remain in finding such mutations. Here, we discuss structure- and cell-based approaches to identify resistance- and sensitivity-conferring mutations. Further, we describe how resistance-conferring mutations can help with compound design, and the use of saturation mutagenesis to characterize a compound binding site. We highlight how genetic approaches can ensure the proper use of chemical inhibitors to pursue mechanistic studies and test therapeutic hypotheses.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are needed to diminish the morbidity of the ongoing COVID-19 pandemic. A well-precedented drug target is the main viral protease (MPro ), which is targeted by an approved drug and by several investigational drugs. Emerging viral resistance has made new inhibitor chemotypes more pressing. Adopting a structure-based approach, we docked 1.2 billion non-covalent lead-like molecules and a new library of 6.5 million electrophiles against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 and 20 μM, respectively. Several series were optimized, resulting in low micromolar inhibitors. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. While the new chemotypes may aid further optimization of MPro inhibitors for SARS-CoV-2, the modest success rate also reveals weaknesses in our approach for challenging targets like MPro versus other targets where it has been more successful, and versus other structure-based techniques against MPro itself.