Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This "smart" SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor's response. This can be explained by considering the aptamers' conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, DiffDock has fast inference times and provides confidence estimates with high selective accuracy.Comment: Under revie

Enhancing Molecular Docking with Deep Q-Networks

El descubrimiento de fármacos es un proceso largo y costoso que suele durar entre 10 y 15 años, desde la evaluación inicial de candidatos farmacológicos hasta la aprobación final por parte de los organismos reguladores correspondientes. Por este motivo, simulaciones moleculares por computador, conocidas como Virtual Screening (VS) (o Cribado Virtual), se utilizan a menudo para predecir los candidatos a fármacos durante las primeras etapas de su desarrollo. Uno de los métodos más utilizados en el VS es el llamado Docking Molecular, o simplemente abreviado como Docking (en español, Acoplamiento Molecular). El objetivo de este método es resolver el problema de las Interacciones Proteína-Ligando (PLDP) o Docking. Dicho de otro modo, se trata de predecir las conformaciones 3D en las que un candidato farmacológico (también conocido como ligando) se acopla a un receptor determinado (normalmente una proteína) en un punto concreto de su superficie. Los métodos tradicionales de Docking se basan en procedimientos de optimización de funciones de puntuación (o de scoring) siguiendo determinadas heurísticas. Se trata de funciones matemáticas que modelan las interacciones moleculares. Estos métodos se caracterizan por ser computacionalmente costosos. De esta manera, en esta tesis se pretende aprovechar los prometedores algoritmos de Deep RL para mejorar la resolución del problema de Docking. Para ello, el hilo conductor de esta tesis doctoral son las diferentes alternativas de representación de las moléculas de la escena de Docking que serán utilizadas como datos de entrada de dichos algoritmos. En consecuencia, primero se replantea el problema PLDP como uno de Aprendizaje por Refuerzo (RL). Acto seguido, se construye un sistema básico basado en el algoritmo de Deep Q-Network (DQN), originalmente diseñado para enseñar a agentes artificiales a jugar a videojuegos de la consola de Atari 2600. En segundo lugar, se utiliza una implementación, denominada QN-Docking, basada en un vector de características sencillo para la representación molecular. Dicha implementación es testada en un entorno con un receptor relativamente pequeño y un espacio de acciones limitado. Los resultados de la fase de predicción muestran que QN-Docking consigue un aumento de velocidad 8 veces mayor en comparación con métodos estocásticos como METADOCK 2. Dicho programa es un nuevo software de alto rendimiento que incluye diversas metaheurísticas para el Acoplamiento Molecular. Por último, una implementación alternativa basada en imágenes, MVDQN, es testada en el mismo escenario que QN-Docking. Los resultados muestran un rendimiento similar al de la primer implementación durante la fase de entrenamiento. Sin embargo, en la fase de predicción los resultados son mixtos. El agente actúa de forma subóptima en varias de las posiciones de partida establecidas en el experimento. Este escenario final parece prometedor, no obstante, ya que hay mucho margen de mejora para seguir puliendo el algoritmo y mejorar la representación molecular. En resumen, estos resultados suponen un valioso hito en el desarrollo de un método basado en Inteligencia Artificial más rápido y efectivo para resolver el problema PLDP en comparación con métodos más tradicionales.Ingeniería, Industria y Construcció

Exploring Chemical Space with Score-based Out-of-distribution Generation

A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set. To generate truly novel molecules that may have even better properties for de novo drug discovery, more powerful exploration in the chemical space is necessary. To this end, we propose Molecular Out-Of-distribution Diffusion(MOOD), a score-based diffusion scheme that incorporates out-of-distribution (OOD) control in the generative stochastic differential equation (SDE) with simple control of a hyperparameter, thus requires no additional costs. Since some novel molecules may not meet the basic requirements of real-world drugs, MOOD performs conditional generation by utilizing the gradients from a property predictor that guides the reverse-time diffusion process to high-scoring regions according to target properties such as protein-ligand interactions, drug-likeness, and synthesizability. This allows MOOD to search for novel and meaningful molecules rather than generating unseen yet trivial ones. We experimentally validate that MOOD is able to explore the chemical space beyond the training distribution, generating molecules that outscore ones found with existing methods, and even the top 0.01% of the original training pool. Our code is available at https://github.com/SeulLee05/MOOD.Comment: ICML 202