Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

Shayakhmetov, Rim; Kuznetsov, M. S.; Zhebrak, Alexander; Kadurin, Artur; Nikolenko, Sergey I.; Aliper, Alexander; Polykovskiy, Daniil

doi:10.3389/fphar.2020.00269

Cited by 32 publications

(44 citation statements)

References 42 publications

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“…Artificial intelligence has been applied to determine the affinity of ligands to target SARS-CoV-2 proteins, though it can also be used to predict the toxicity of a given drug via training on publicly available toxicity datasets (TOXNET or ToxCast) and empirically or structurally calculated molecular descriptors [ 173 ]. Adversarial autoencoder (AAE) algorithms have been used to design novel inhibitors based on existing templates and desired gene expression profiles [ 174 ]. Although quite powerful, AI faces many barriers to implementation in drug development that AI proponents are trying to overcome [ 175 ].…”

Section: Current Challenges and Future Perspectivesmentioning

confidence: 99%

Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

et al. 2021

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There is a pressing need for effective therapeutics for coronavirus disease 2019 (COVID-19), the respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The process of drug development is a costly and meticulously paced process, where progress is often hindered by the failure of initially promising leads. To aid this challenge, in vitro human microphysiological systems need to be refined and adapted for mechanistic studies and drug screening, thereby saving valuable time and resources during a pandemic crisis. The SARS-CoV-2 virus attacks the lung, an organ where the unique three-dimensional (3D) structure of its functional units is critical for proper respiratory function. The in vitro lung models essentially recapitulate the distinct tissue structure and the dynamic mechanical and biological interactions between different cell types. Current model systems include Transwell, organoid and organ-on-a-chip or microphysiological systems (MPSs). We review models that have direct relevance toward modeling the pathology of COVID-19, including the processes of inflammation, edema, coagulation, as well as lung immune function. We also consider the practical issues that may influence the design and fabrication of MPS. The role of lung MPS is addressed in the context of multi-organ models, and it is discussed how high-throughput screening and artificial intelligence can be integrated with lung MPS to accelerate drug development for COVID-19 and other infectious diseases.

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Section: Current Challenges and Future Perspectivesmentioning

confidence: 99%

Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

et al. 2021

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“…Computational methods are efficient, accurate, holistic (i.e., take into account the entire interaction space of chemical entities), and have breadth in terms of chemical space exploration necessary to overcome the limitations of traditional approaches [2,6,12,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. To expand compound libraries utilized in screening, combinatorial chemistry and machine-learning design pipelines have been developed to generate libraries of compounds likely to bind to a given target [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Various encoder-decoder models [40][41][42] for conditional [43] molecular generation on multiple properties have been proposed [17,[44][45][46], but in most cases, these properties are limited to physiochemical ones. These models, however, do show great promise in their ability to rapidly generate compounds with desired properties.…”

Section: Introductionmentioning

confidence: 99%

“…These models, however, do show great promise in their ability to rapidly generate compounds with desired properties. The most sophisticated conditional molecular generation performed thus far to our knowledge is inducing a differential expression profile of several hundred genes [17]. In all these models, proteins, the functional molecules that are primarily bound by human-ingested drugs to ensure efficacy and ADMET, remain to be considered explicitly on a large scale.…”

Section: Introductionmentioning

confidence: 99%

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A Deep-Learning Proteomic-Scale Approach for Drug Design

et al. 2021

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Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

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“…Computational methods are efficient, accurate, holistic (i.e., take into account the entire interaction space of chemical entities), and have breadth in terms of chemical space exploration that are necessary to overcome the limitations of traditional approaches [2,6,12,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. To expand compound libraries utilized in screening, combinatorial chemistry and machine learning design pipelines have been developed to generate libraries of compounds likely to bind to a given target [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Proteomic Scale Approach for Drug Design

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Mangione

et al. 2021

Preprint

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Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multi-target therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach by computing interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning based autoencoder to first reduce the dimensionality of CANDO computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this model, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds are predicted to be significantly (p-value ≤ .05) more behaviorally similar relative to all corresponding controls, and 20/20 are predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design perform significantly better than those derived from natural sources (p-value ≤.05), suggesting that the model has learned an abstraction of rational drug design. We also show that designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhance thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. This work represents a significant step forward in automating holistic therapeutic design with machine learning, and subsequently offers a reduction in the time needed to generate novel, effective, and safe drug leads for any indication.

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Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

Cited by 32 publications

References 42 publications

Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

Application of lung microphysiological systems to COVID-19 modeling and drug discovery: a review

A Deep-Learning Proteomic-Scale Approach for Drug Design

A Deep Learning Proteomic Scale Approach for Drug Design

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