Lead optimization is one of the crucial steps in the drug discovery pipeline. After identifying the lead molecule and obtaining its 2D geometry, understanding the best conformation it would attain in 3D still remains one of the most challenging steps in drug discovery. There have been multiple methods and algorithms that are directed toward achieving best conformation for the lead molecules. TANGO focuses on conformation generation and its optimization using semiempirical energy calculations. The conformation generation is based on torsion angle rotation of the exocyclic bonds. The energy calculations are performed using MOPAC. The unique feature of this tool lies in the implementation of Message Passing Interface (MPI) for conformation generation and semiempirical‐based optimization. A well‐defined architecture handling the input and output generation has been used. The master and slave approach to handle operations involved in torsion angle rotation and energy calculations has helped in load balancing the process of conformation generation. The benchmarking results suggest that TANGO scales significantly well across eight nodes with each node utilizing 16 cores. This tool may prove to very useful in high throughput generation of semiempirically optimized small molecule conformations. The use of semiempirical methods for optimization generates a conformational ensemble thereby helping to obtain stable and alternate stable conformers for a given ligand molecule. © 2018 Wiley Periodicals, Inc.

中文翻译：

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TANGO：一种高通量构象生成和基于半经验方法的配体分子优化工具

先导优化是药物发现管道中的关键步骤之一。在确定先导分子并获得其 2D 几何形状后，了解它在 3D 中将获得的最佳构象仍然是药物发现中最具挑战性的步骤之一。有多种方法和算法旨在实现先导分子的最佳构象。TANGO 专注于使用半经验能量计算的构象生成及其优化。构象生成基于环外键的扭转角旋转。使用 MOPAC 执行能量计算。该工具的独特之处在于实现了用于构造生成和半经验优化的消息传递接口 (MPI)。已经使用了一个定义良好的架构来处理输入和输出生成。处理涉及扭转角旋转和能量计算的操作的主从方法有助于负载平衡构象生成过程。基准测试结果表明 TANGO 在 8 个节点上的扩展性非常好，每个节点使用 16 个内核。该工具可能被证明在半经验优化小分子构象的高通量生成中非常有用。使用半经验方法进行优化会产生构象集合，从而有助于为给定的配体分子获得稳定和替代的稳定构象异构体。© 2018 Wiley Periodicals, Inc. 处理涉及扭转角旋转和能量计算的操作的主从方法有助于负载平衡构象生成过程。基准测试结果表明 TANGO 在 8 个节点上的扩展性非常好，每个节点使用 16 个内核。该工具可能被证明在半经验优化小分子构象的高通量生成中非常有用。使用半经验方法进行优化会产生构象集合，从而有助于为给定的配体分子获得稳定和替代的稳定构象异构体。© 2018 Wiley Periodicals, Inc. 处理涉及扭转角旋转和能量计算的操作的主从方法有助于负载平衡构象生成过程。基准测试结果表明 TANGO 在 8 个节点上的扩展性非常好，每个节点使用 16 个内核。该工具可能被证明在半经验优化小分子构象的高通量生成中非常有用。使用半经验方法进行优化会产生构象集合，从而有助于为给定的配体分子获得稳定和替代的稳定构象异构体。© 2018 Wiley Periodicals, Inc. 该工具可能被证明在半经验优化小分子构象的高通量生成中非常有用。使用半经验方法进行优化会产生构象集合，从而有助于为给定的配体分子获得稳定和替代的稳定构象异构体。© 2018 Wiley Periodicals, Inc. 该工具可能被证明在半经验优化小分子构象的高通量生成中非常有用。使用半经验方法进行优化会产生构象集合，从而有助于为给定的配体分子获得稳定和替代的稳定构象异构体。© 2018 Wiley Periodicals, Inc.