| ²é¿´: 400 | »Ø¸´: 0 | |||||||||||
| ¡¾Óн±½»Á÷¡¿»ý¼«»Ø¸´±¾Ìû×Ó£¬²ÎÓë½»Á÷£¬¾ÍÓлú»á·ÖµÃ×÷Õß Å¬Á¦´ò¹¤µÄ´ò¹¤È µÄ 5 ¸ö½ð±Ò | |||||||||||
ŬÁ¦´ò¹¤µÄ´ò¹¤Èгæ (³õÈëÎÄ̳)
|
[½»Á÷]
¼ÆËãÉúÎï¡¢»¯Ñ§¡¢ÉúÎïÐÅÏ¢¡¢½á¹¹ÉúÎïѧ·½Ïò
|
||||||||||
Ò»¡¢aidd/cadd scientist¡úleader-ÉϺ£ responsibilities: 1. support/drive early discovery projects from target identification/evaluation to hit identification, to lead optimization, and all the way to pcc and ind-enabling studies 2. contribute to the hit/lead optimization campaigns through mitigating any detected liabilities in terms of potency, selectivity, and adme/pk profiles 3. contribute to the global ai platform in optimizing current tools or establishing novel ai/machine learning models in cheminformatics and/or molecular modeling areas, which are applicable to discovery projects 4. proactively integrate experimental data and apply computational approaches to generate knowledge and insights requirements: 1. phd in computational chemistry/ biology, cadd, or any relevant areas that also bring the following set of skills and expertise 2. proficient in structure-/ligand-based drug design, including molecular docking, virtual screening, qsar, homology modeling, molecular dynamics, and quantum mechanics, etc., and master the relevant tools, including schrodinger, moe, discovery studio, etc. 3. proficient command of at least one programming language (python, c, c++, perl, shell, ¡) 4. highly motivated, possess excellent communication skills, enjoy working in multidisciplinary teams and are fluent in english 5. experience in ai/machine learning, data mining, and automate workflows is a plus 6. experience in the pharmaceutical industry or postdoctoral research in the drug discovery context would be a plus 7. good understanding of drug discovery principles including the fundamentals of medicinal chemistry, adme/pk and disease biology is a big plus 8. a track record of scientific contributions illustrated by publications and/or patents. extra points 1.expertise in machine learning (ml) and deep learning (dl), with experience in developingor applying ml/dl methods in at least one of the following fields: molecular propertyprediction, molecular generation, retrosynthesis prediction, protein design, biomedical textmining, or bioinformatics analysis, etc 2.experience in antibody and peptide generation or small molecule drug design for ppi would be a big plus. ¶þ¡¢¿¹ÌåÉè¼Æ-±±¾© Ö÷Òª¹¤×÷Ö°Ô𣺠1.»ùÓÚµ°°×Öʽṹ·ÖÎöºÍÔ¤²â£¬¿¹Ô¿¹Ìå¶Ô½ÓµÈÊֶΣ¬Éè¼ÆµãÍ»±ä»òÕßÎĿ⣬´Ó¶à¸öά¶ÈÓÅ»¯¿¹ÌåÒ©ÎïµÄÌØÐÔ£¬É¸Ñ¡ÓÐÖÎÁÆÇ±Á¦µÄ·Ö×Ó£» 2.¿¹ÌåÈËÔ´»¯£¬Ç׺ÍÁ¦³ÉÊ죬Îȶ¨ÐÔÓÅ»¯£¬Ë«ÌØÒ쿹ÌåµÄÉè¼ÆµÈ£» 3.Íê³É·Ö×Ó¿Ë¡µÄ¹¹½¨£¬ºÍÍŶÓÒ»ÆðÑéÖ¤µ°°×ÖÊÓÅ»¯µÄÉè¼Æ£» 4.¼°Ê±·ÖÎö»ã±¨Êý¾Ý£¬°ïÖúÏîÄ¿×é×ö¾ö¶¨£» Ö÷Òª½»¸¶½á¹û 1.×÷ΪÖÎÁÆÐÔ¿¹Ìå·¢ÏÖÍŶӵÄÒ»Ô±£¬Íê³É¿¹ÌåÒ©ÎïµÄÓÅ»¯Éè¼Æ²¢×îÖÕ½»¸¶pcc·Ö×Ó£» 2.¶ÀÁ¢Íê³Éµ°°×Ò©ÎïµÄÉè¼ÆºÍÏàÓ¦µÄ¿Ë¡±í´ï¹¤×÷£» 3.°´Ê±½»¸¶½á¹û£» ÈÎÖ°×ʸñ ½ÌÓýºÍ¹¤×÷±³¾°: 1.µ°°×Éè¼Æ£¬½á¹¹ÉúÎïѧÏà¹ØÁìÓò²©Ê¿ÒÔÉÏѧÀú£¬ 2ÄêÒÔÉϹ¤×÷¾Ñ飻 2.ÉîÈëÁ˽⵰°×½á¹¹ºÍ¹¦ÄÜ£¬¾«Í¨Ò»¸ö»ò¶à¸ö³£Óõĵ°°×Öʹ¤³Ì·Ö×Ó½¨Ä£Èí¼þ°ü £¨schrodinger, rosetta, moe, discovery studio£©£» 3.Óе°°×Öʹ¤³Ì»¯¸ÄÔìµÄÑо¿¾Ñ飬ÈçÌá¸ßÎȶ¨ÐÔ£¬¿¹ÌåÈËÔ´»¯£¬Ç׺ÍÁ¦³ÉÊì£¬Ë«ÌØÒ쿹ÌåÉè¼ÆµÈ£» 4.ÊìÁ·ÕÆÎÕ·Ö×ÓÉúÎïѧ¼¼Êõ£¬µ°°×±í´ï´¿»¯¼¼Êõ¡£ 5.ÓÐË«¿¹/¶à¿¹Éè¼Æ¾ÑéÕßÓÅÏÈ¿¼ÂÇ Í¶µÝÇë±ê×¢£ºXX¸Úλ+ÐÕÃû+±ÏҵԺУ+×î¸ßѧλ KSX0928 charich.yp@x-giants.com [ À´×Ô°æ¿éȺ ÉϺ£ ] |
» ²ÂÄãϲ»¶
Çó¸ö²©µ¼¿´¿´
ÒѾÓÐ17È˻ظ´
Çà»ù´ú±í×÷£¬AAAIÖ®ÀàµÄA»áµÄspecial trackÔÚ¹úÄÚÈϿɶȸßÂ𣿻¹ÊǹéΪworkshopÖ®Á÷£¿
ÒѾÓÐ3È˻ظ´
ÉϺ£¹¤³Ì¼¼Êõ´óѧ¡¾¼¤¹âÖÇÄÜÖÆÔì¡¿¿ÎÌâ×éÕÐÊÕ˶ʿ
ÒѾÓÐ6È˻ظ´
´ø×ʽø×éÇ󲩵¼ÊÕÁô
ÒѾÓÐ11È˻ظ´
×Ô¼ö¶Á²©
ÒѾÓÐ5È˻ظ´
ÉϺ£¹¤³Ì¼¼Êõ´óѧÕÅÅàÀÚ½ÌÊÚÍŶÓÕÐÊÕ²©Ê¿Éú
ÒѾÓÐ4È˻ظ´
ÇóÖúԺʿÃÇ£¬Õâ¸öÈçºÎºÏ³Éѽ
ÒѾÓÐ4È˻ظ´
ÁÙ¸ÛʵÑéÊÒÓëÉϿƴóÁªÅ಩ʿÕÐÉú1Ãû
ÒѾÓÐ9È˻ظ´
дÁËһƪ¡°Ïà±ä´¢Äܼ¼ÊõÔÚÀä¿âÖÐÓ¦Óá±µÄÂÛÎÄ£¬ÂÛÎÄÄÚÈÝÒÔʵÑéΪÖ÷£¬Í¶Ê²Ã´ÆÚ¿¯ºÏÊÊ£¿
ÒѾÓÐ6È˻ظ´
×î½ü¼¸ÄêÕеÄѧÉúдÂÛÎIJ»Òý×Ô¼º×é·¢µÄÎÄÕÂ
ÒѾÓÐ11È˻ظ´







»Ø¸´´ËÂ¥