Recently, deep generative models for molecular graphs are gaining more and more attention in the field of de novo drug design. A variety of models have been developed to generate topological structures of drug-like molecules, but explorations in generating three-dimensional structures are still limited. Existing methods have either focused on low molecular weight compounds without considering drug-likeness or generate 3D structures indirectly using atom density maps. In this work, we introduce Ligand Neural Network (L-Net), a novel graph generative model for designing drug-like molecules with high-quality 3D structures. L-Net directly outputs the topological and 3D structure of molecules (including hydrogen atoms), without the need for additional atom placement or bond order inference algorithm. The architecture of L-Net is specifically optimized for drug-like molecules, and a set of metrics is assembled to comprehensively evaluate its performance. The results show that L-Net is capable of generating chemically correct, conformationally valid, and highly druglike molecules. Finally, to demonstrate its potential in structure-based molecular design, we combine L-Net with MCTS and test its ability to generate potential inhibitors targeting ABL1 kinase.
Structure-based drug design involves finding ligand molecules that exhibit structural and chemical complementarity to protein pockets. Deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design), avoiding exhaustive virtual screening of chemical space. Most generative de-novo models fail to incorporate detailed ligand-protein interactions and 3D pocket structures. We propose a novel supervised model that generates molecular graphs jointly with 3D pose in a discretised molecular space. Molecules are built atom-by-atom inside pockets, guided by structura…
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To addres…
Novartis Institutes for BioMedical Research（诺华生物医学研究所）
The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2…
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. Th…
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although current graph generative models are available, they are often too general and computationally expensive, which restricts their application to molecules with small sizes. In this work, a new de novo molecular design framework is proposed based on a type sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, th…
Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系生物数据科学实验室）；Dept. of Computer Engineering, Hacettepe University（哈切塔佩大学计算机工程系）；Dept. of Chemistry, Middle East Technical University（中东技术大学化学系）；Dept. of Physics, Middle East Technical University（中东技术大学物理系）；Dept. Of Artificial Intelligence Engineering, Bahcesehir University（巴切塞希尔大学人工智能工程系）；Cancer Systems Biology Lab, Graduate School of Informatics, Middle East Technical University（中东技术大学信息研究生院癌症系统生物学实验室）；Dept. of Pharmaceutical Chemistry, Faculty of Pharmacy, Gazi University（加齐大学药学系药剂化学系）；Laboratory of Molecular Modeling, Evias Pharmaceutical R&D Ltd.（Evias制药研发有限公司分子建模实验室）；Dept. of Electrical and Electronics Engineering, İskenderun Technical University（伊斯肯德伦技术大学电子与电气工程系）；Institute for Computational Biomedicine, Heidelberg University（海德堡大学计算生物医学研究所）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（哈切塔佩大学健康科学研究生院生物信息学系）
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, …
Tsinghua University（清华大学）；Ping An Technology（平安科技）；Beijing University of Chemical Technology（北京化工大学）；Lanzhou University（兰州大学）
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful MolGNet model and an effective self-supervised strategy for pre-tra…
Baidu Inc.（百度公司）；University of Science and Technology of China（中国科学技术大学）；Rutgers, The State University of New Jersey（罗格斯大学（新泽西州立大学））
Accurately predicting the binding affinity between drugs and proteins is an essential step for computational drug discovery. Since graph neural networks (GNNs) have demonstrated remarkable success in various graph-related tasks, GNNs have been considered as a promising tool to improve the binding affinity prediction in recent years. However, most of the existing GNN architectures can only encode the topological graph structure of drugs and proteins without considering the relative spatial information among their atoms. Whereas, different from other graph datasets such as social networks and co…
Peking University（北京大学）；School of Pharmaceutical Sciences, Peking University（北京大学药学院）
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the …
University of California, Berkeley（加州大学伯克利分校）；University of Chicago（芝加哥大学）；University of Notre Dame（圣母大学）
In the past several months, COVID-19 has spread over the globe and caused severe damage to the people and the society. In the context of this severe situation, an effective drug discovery method to generate potential drugs is extremely meaningful. In this paper, we provide a methodology of discovering potential drugs for the treatment of Severe Acute Respiratory Syndrome Corona-Virus 2 (commonly known as SARS-CoV-2). We proposed a new model called Genetic Constrained Graph Variational Autoencoder (GCGVAE) to solve this problem. We trained our model based on the data of various viruses' protein…
Diff4VS: HIV-inhibiting Molecules Generation with Classifier Guidance Diffusion for Virtual Screening
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Innovation and Entrepreneurship, Dalian University of Technology（大连理工大学创新创业学院）
Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds with desirable pharmacological properties and cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multi-property optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate and predict drug-like molecules with desired chemical properties. To further validate the…
Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation. With their outstanding performance in de novo drug design, deep generative models represent promising tools for tackling this challenge. In recently years, 3D molecule generative models have gained increasing attention due to their ability to directly utilize the 3D interaction information between the target and ligand. However, it remains challenging to synthesize the molecules generated by these models, limiting the speed of bio…
IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research Europe（IBM欧洲研究院）
The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules f…
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such generative models is notoriously difficult. Metrics that are typically used to assess the performance of such generative models are the percentage of chemically valid molecules or the similarity to real molecules in terms of particular descriptors, such as the partition coefficient (logP) or druglikeness. However, method comparison is difficult because of the inconsistent use of evaluation metrics, the necessity for …
Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source that encodes human knowledge of drug relations in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding …
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Edwardson School of Industrial Engineering and the Institute of Cancer Research, Purdue University（埃德沃兹工业工程学院和癌症研究所，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家庭电气与计算机工程学院，普渡大学）
University of Padua（帕多瓦大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.
Assay2Mol: large language model-based drug design using BioAssay context
Department of Computer Sciences, University of Wisconsin-Madison（威斯康星大学麦迪逊分校计算机科学系）；Morgridge Institute for Research（摩尔格德研究所）；Drug Development Core, Small Molecule Screening Facility, University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison（威斯康星大学卡本癌症中心药物开发核心、小分子筛选设施）；Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校生物统计学与医学信息学系）
Harvard University（哈佛大学）；Georgia Institute of Technology（佐治亚理工学院）；Gradio；IQVIA；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug efficacy. We develop MolDesigner, a human-in-the-loop web user-interface (UI), to assist drug developers leverage DL predictions to design more effective drugs. A developer can draw a drug molecule in the interface. In the backend, more than 17 state-of-the-art DL models generate predictions on important indices that are crucial for a drug's efficacy. Based on these predictions, drug developers can edit the drug molecu…
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback
Regenerative Medicine Research Center, Isfahan University of Medical Sciences（再生医学研究中心，伊斯法罕医科大学）；Department of Medicinal Chemistry, School of Pharmacy, Isfahan University of Medical Sciences（药理学系，药学院，伊斯法罕医科大学）；Department of Genetics and Molecular Biology, Isfahan University of Medical Sciences（遗传学与分子生物学系，伊斯法罕医科大学）
Generating novel drug molecules with desired biological properties is a time consuming and complex task. Conditional generative adversarial models have recently been proposed as promising approaches for de novo drug design. In this paper, we propose a new generative model which extends an existing adversarial autoencoder (AAE) based model by stacking two models together. Our stacked approach generates more valid molecules, as well as molecules that are more similar to known drugs. We break down this challenging task into two sub-problems. A first stage model to learn primitive features from th…
A cooperative drug delivery system is proposed, where quorum sensing (QS), a density-dependent bacterial behavior coordination mechanism, is employed by synthetic bacterium-based nanomachines (B-NMs) for controllable drug delivery. In our proposed system, drug delivery is only triggered when there are enough QS molecules, which in turn only happens when there are enough B-NMs. This makes the proposed system can be used to achieve a high release rate of drug molecules from a high number of B-NMs when the population density of B-NMs may not be known. Analytical expressions for i) the expected ac…
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings and molecular graphs. In this work, we describe deep generative models of three dimensional molecular structures using atomic density grids and a novel fitting algorithm for converting continuous grids to discrete molecular structures. Our…
DrugGen 2: A disease-aware language model for enhancing drug discovery
Regenerative Medicine Research Center（再生医学研究中心）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan Neuroscience Research Center（伊斯法罕神经科学研究中心）；School of Pharmacy（药学院）；Department of Medicinal Chemistry（药理化学系）；Cardiovascular Research Institute（心血管研究学院）；Department of Genetics and Molecular Biology（基因与分子生物学系）；Bioinformatics Research Center（生物信息学研究中心）
Virus infection is one of the major diseases that seriously threaten human health. To meet the growing demand for mining and sharing data resources related to antiviral drugs and to accelerate the design and discovery of new antiviral drugs, we presented an open-access antiviral drug resource and machine learning platform (VDDB), which, to the best of our knowledge, is the first comprehensive dedicated resource for experimentally verified potential drugs/molecules based on manually curated data. Currently, VDDB highlights 848 clinical vaccines, 199 clinical antibodies, as well as over 710,000 …
Since the rise of the SARS-CoV-2 pandemic in the winter of 2019, the need for an affordable and efficient drug has not yet been met. Leveraging its unique, fast and precise binding free energy prediction technology, Aqemia screened and ranked FDA-approved molecules against the 3ClPro protein. This protease is key to the post-translational modification of two polyproteins produced by the viral genome. We propose in our top 10 predicted molecules some drugs or prodrugs that could be repurposed and used in the treatment of COVID cases.
De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the molecules of the training set or molecules that are optimized with respect to specific physicochemical properties. None of the methods generates molecules specific to a target protein. In the approach presented here, we introduce a method which is conditioned on the protein target sequence to generate de novo molecules that are relevant to the target. We use an implementation adapted from Google's "Show and Tell" ima…
Background: The volume of distribution at steady state (VDss) is a fundamental pharmacokinetics (PK) property of drugs, which measures how effectively a drug molecule is distributed throughout the body. Along with the clearance (CL), it determines the half-life and, therefore, the drug dosing interval. However, the molecular data size limits the generalizability of the reported machine learning models. Objective: This study aims to provide a clean and comprehensive dataset for human VDss as the benchmarking data source, fostering and benefiting future predictive studies. Moreover, several pred…
